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Yeom, Chan-Uk, and Keun-Chang Kwak. "Performance Comparison of ANFIS Models by Input Space Partitioning Methods." Symmetry 10, no. 12 (December 3, 2018): 700. http://dx.doi.org/10.3390/sym10120700.
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In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.
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Mousavi, S. F., and M. J. Amiri. "Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system." Soil and Water Research 7, No. 2 (May 18, 2012): 73–83. http://dx.doi.org/10.17221/46/2010-swr.
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High nitrate concentration in groundwater is a major problem in agricultural areas in Iran. Nitrate pollution in groundwater of the particular regions in Isfahan province of Iran has been investigated. The objective of this study was to evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) for estimating the nitrate concentration. In this research, 175 observation wells were selected and nitrate, potassium, magnesium, sodium, chloride, bicarbonate, sulphate, calcium and hardness were determined in groundwater samples for five consecutive months. Electrical conductivity (EC) and pH were also measured and the sodium absorption ratio (SAR) was calculated. The five-month average of bicarbonate, hardness, EC, calcium and magnesium are taken as the input data and the nitrate concentration as the output data. Based on the obtained structures, four ANFIS models were tested against the measured nitrate concentrations to assess the accuracy of each model. The results showed that ANFIS1 was the most accurate (RMSE = 1.17 and R<sup>2</sup> = 0.93) and ANFIS4 was the worst (RMSE = 2.94 and R<sup>2</sup> = 0.68) for estimating the nitrate concentration. In ranking the models, ANFIS2 and ANFIS3 ranked the second and third, respectively. The results showed that all ANFIS models underestimated the nitrate concentration. In general, the ANFIS1 model is recommendable for prediction of nitrate level in groundwater of the studied region.
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Yeom, Chan-Uk, and Keun-Chang Kwak. "Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach." Applied Sciences 10, no. 23 (November 27, 2020): 8495. http://dx.doi.org/10.3390/app10238495.
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We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, multiple ANFISs were designed in an incremental tree structure without using a single ANFIS. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset. The prediction experiment verified that the proposed CFCM-clustering-based ANFIS shows better prediction efficiency than the current grid-based and clustering-based ANFISs in the form of an incremental tree.
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Tien Bui, Dieu, Khabat Khosravi, Shaojun Li, Himan Shahabi, Mahdi Panahi, Vijay Singh, Kamran Chapi, et al. "New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling." Water 10, no. 9 (September 7, 2018): 1210. http://dx.doi.org/10.3390/w10091210.
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This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains.
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A-Matarneh, Feras Mohammed, Bassam A. Y. Alqaralleh, Fahad Aldhaban, Esam A. AlQaralleh, Anil Kumar, Deepak Gupta, and Gyanendra Prasad Joshi. "Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks." Computational Intelligence and Neuroscience 2022 (May 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/7940895.
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Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively.
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Rezk, Hegazy, A. G. Olabi, Mohammad Ali Abdelkareem, Abdul Hai Alami, and Enas Taha Sayed. "Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm." Sustainability 15, no. 2 (January 13, 2023): 1589. http://dx.doi.org/10.3390/su15021589.
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Hydrogen is a new promising energy source. Three operating parameters, including inlet gas flow rate, pH and impeller speed, mainly determine the biohydrogen production from membrane bioreactor. The work aims to boost biohydrogen production by determining the optimal values of the control parameters. The proposed methodology contains two parts: modeling and parameter estimation. A robust ANIFS model to simulate a membrane bioreactor has been constructed for the modeling stage. Compared with RMS, thanks to ANFIS, the RMSE decreased from 2.89 using ANOVA to 0.0183 using ANFIS. Capturing the proper correlation between the inputs and output of the membrane bioreactor process system encourages the constructed ANFIS model to predict the output performance exactly. Then, the optimal operating parameters were identified using the honey badger algorithm. During the optimization process, inlet gas flow rate, pH and impeller speed are used as decision variables, whereas the biohydrogen production is the objective function required to be maximum. The integration between ANFIS and HBA boosted the hydrogen production yield from 23.8 L to 25.52 L, increasing by 7.22%.
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Abdallah, El, Maamar Laidi, and Salah Hanini. "New method based on neuro-fuzzy system and PSO algorithm for estimating phase equilibria properties." Chemical Industry and Chemical Engineering Quarterly, no. 00 (2021): 24. http://dx.doi.org/10.2298/ciceq201104024a.
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The subject of this work is to propose a new method based on ANFIS system and PSO algorithm to conceive a model for estimating the solubility of solid drugs in sc-CO2. The high nonlinear process was modeled by neuro-fuzzy approach (NFS). The PSO algorithm was used for two purposes: replacing the standard back propagation in training the NFS and optimizing the process. The validation strategy have been carried out using a linear regression analysis of the predicted versus experimental outputs. The ANFIS approach is compared to the ANN in terms of accuracy. Statistical analysis of the predictability of the optimized model trained with PSO algorithm (ANFIS-PSO) shows very good agreement with reference data than ANN method. Furthermore, the comparison in terms of AARD deviation (%) between the predicted results, results predicted by density-based models and a set of equations of state demonstrates that the ANFIS-PSO model correlates far better the solubility of the solid drugs in scCO2.A control strategy was also developed for the first time in the field of phase equilibrium by using the neuro fuzzy inverse approach (ANFISi) to estimate pure component properties from the solubility data without passing through GCM methods.
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Rezakazemi, Mashallah, Amir Dashti, Morteza Asghari, and Saeed Shirazian. "H2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS." International Journal of Hydrogen Energy 42, no. 22 (June 2017): 15211–25. http://dx.doi.org/10.1016/j.ijhydene.2017.04.044.
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Bassetto, Edson Luis, João Francisco Escobedo, and Alexandre Dal Pai. "ESTIMATIVA DA FRAÇÃO DIFUSA DA IRRADIAÇÃO GLOBAL COM TÉCNICAS DE APRENDIZAGEM DE MÁQUINAS." Revista Brasileira de Energia Solar 9, no. 2 (February 13, 2023): 127–36. http://dx.doi.org/10.59627/rbens.2018v9i2.242.
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Foram desenvolvidos modelos de estimativa da fração difusa (Kd) em função da fração transmitida da irradiação global (Kt) na partição horária sendo: Modelo Estatístico (ME); Redes Neurais Artificias com Função de Base Radial (RBF); e Sistema Adaptativo de Inferência Neuro Fuzzy (ANFIS). O modelo estatístico utiliza como referência somente Kt e as técnicas uma combinação de seis (06) variáveis astronômicas, geográficas e meteorológicas. Os modelos utilizam uma base de sete anos (2000-2006) de medidas realizadas na Estação de Radiometria Solar de Botucatu/SP na partição horária, sendo parte para treinamento e outra para validação dividida em Ano típico (AT) e atípico (AAT). A equação do modelo estatístico gerada por regressão polinomial de 4ª ordem, apresenta coeficiente de determinação R2 = 0.80 e na comparação dos valores medidos e estimados na validação, um coeficiente de correlação para ano típico (AT) rAT=0.90 e para o ano atípico (AAT) de rAAT=0.89, erro quadrático médio rRMSEAT = 30.55% e rRMSEAAT = 27.97%. No desempenho das técnicas RBF e ANFIS, os modelos mostraram-se satisfatórios a partir da segunda combinação sendo para RBF2 um coeficiente rAT=0.91 e rAAT=0.90 e erro de rRMSEAT = 29.63% e rRMSEAAT = 26.93% e para ANFIS2 um rAT=0.93 e rAAT=0.93 com erro rRMSEAT = 25.13% e rRMSEAAT = 22.76%. Para sexta combinação, a rede RBF6 um coeficiente de rAT=0.92 e rAAT=0.92 e erro de rRMSEAT = 26.48% e rRMSEAAT = 24.69% e para ANFIS6 um coeficiente rAT=0.95 e rAAT=0.94 e erro de rRMSEAT = 22.63% e rRMSEAAT = 21.19%. Os indicadores mostram que as técnicas de aprendizagem de máquinas comparadas com modelo estatístico apresentaram um desempenho melhor com redução nos indicadores na ordem de 16% para rede RBF6 e 34% para rede ANFIS6 do erro quadrático médio para duas bases de validação (AT e AAT) em relação ao modelo ME.
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Zhang, X. Y., and B. Wei. "A OPTIMIZATION TUNED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR DAM DEFORMATION PREDICTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1207–13. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1207-2020.
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Abstract. The performance and stability of Adaptive Neuro-Fuzzy Inference System (ANFIS) depend on its network structure and preset parameter selection, and Particle Swarm Optimization-ANFIS (PSO-ANFIS) easily falls into the local optimum and is imprecise. A novel ANFIS algorithm tuned by Chaotic Particle Swarm Optimization (CPSO-ANFIS) is proposed to solve these problems. A chaotic ergodic algorithm is first used to improve the PSO and obtain a CPSO algorithm, and then the CPSO is used to optimize the parameters of ANFIS to avoid falling into the local optimum and improve the performance of ANFIS. Based on the deformation data from the Xiaolangdi Dam in China, three neural network algorithms, ANFIS, PSO-ANFIS, and CPSO-ANFIS, are used to establish the dam deformation prediction models after data preparation and selection of influencing factors for the dam deformation. The results are compared using evaluation indicators that show that CPSO-ANFIS is more accurate and stable than ANFIS and PSO-ANFIS both in predictive ability and in predicted results.
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Mohamed, R. A. "Modeling the electrical properties of heterojunctions using ANFIS, ANFIS-GA and ANFIS-PSO Models." Physica Scripta 98, no. 12 (November 3, 2023): 126002. http://dx.doi.org/10.1088/1402-4896/ad05ae.
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Abstract The present research introduces a theoretical study that aims to utilize ANFIS in estimating and predicting the electrical behavior of heterojunctions. For this purpose, five different heterojunctions were chosen. The experimental datasets that represent the electrical behavior of the chosen heterojunctions were extracted and employed in ANFIS as targets. To enhance the ANFIS performance two hybrid heuristic algorithms, genetic algorithm (GAs) and particle swarm optimization (PSO) were combined with ANFIS. The major contribution of the current research is to predict the electric characteristics of heterojunctions using ANFIS and increase the modeling accuracy of ANFIS by optimizing the premise and consequent parameters using (GAs) and (PSO). Also, compare the proportion of enhancement produced by using ANFIS-GA and ANFIS-PSO to decide which of them is more powerful under the study conditions. However, to the author’s knowledge, the presented goals have not been investigated before for heterojunctions. The mean squared error (MSE), the correlation coefficient (R2), and the standard deviation error (Std. error) were calculated for all trained models. The modeling errors of ANFIS-GA and ANFIS-PSO were compared to the error values produced by ANFIS. According to modeling results, simulation ANFIS outputs follow the experimental data patterns in excellent response. Predictions of electrical characteristics for heterojunctions using the trained models provide acceptable results where the MSE values obtained by training ANFIS-PSO are lower than their values obtained by ANFIS and ANFIS-GA models. The improvements in average percentages in ANFIS performance when combined with GA and PSO are equal to 2.2% and 3%, respectively. Consequently, the proposed ANFIS-PSO model is more accurate in predicting the electrical behavior of heterojunctions under the study conditions.
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Adewuyi, Oludamilare Bode, Komla A. Folly, David T. O. Oyedokun, and Emmanuel Idowu Ogunwole. "Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization." Sustainability 14, no. 22 (November 21, 2022): 15448. http://dx.doi.org/10.3390/su142215448.
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In the current era of e-mobility and for the planning of sustainable grid infrastructures, developing new efficient tools for real-time grid performance monitoring is essential. Thus, this paper presents the prediction of the voltage stability margin (VSM) of power systems by the critical boundary index (CBI) approach using the machine learning technique. Prediction models are based on an adaptive neuro-fuzzy inference system (ANFIS) and its enhanced model with particle swarm optimization (PSO). Standalone ANFIS and PSO-ANFIS models are implemented using the fuzzy ‘c-means’ clustering method (FCM) to predict the expected values of CBI as a veritable tool for measuring the VSM of power systems under different loading conditions. Six vital power system parameters, including the transmission line and bus parameters, the power injection, and the system voltage derived from load flow analysis, are used as the ANFIS model implementation input. The performances of the two ANFIS models on the standard IEEE 30-bus and the Nigerian 28-bus systems are evaluated using error and regression analysis metrics. The performance metrics are the root mean square error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (R) analyses. For the IEEE 30-bus system, RMSE is estimated to be 0.5833 for standalone ANFIS and 0.1795 for PSO-ANFIS; MAPE is estimated to be 13.6002% for ANFIS and 5.5876% for PSO-ANFIS; and R is estimated to be 0.9518 and 0.9829 for ANFIS and PSO-ANFIS, respectively. For the NIGERIAN 28-bus system, the RMSE values for ANFIS and PSO-ANFIS are 5.5024 and 2.3247, respectively; MAPE is 19.9504% and 8.1705% for both ANFIS and PSO-ANFIS variants, respectively, and the R is estimated to be 0.9277 for ANFIS and 0.9519 for ANFIS-PSO, respectively. Thus, the PSO-ANFIS model shows a superior performance for both test cases, as indicated by the percentage reduction in prediction error, although at the cost of a higher simulation time.
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Kayhomayoon, Zahra, Faezeh Babaeian, Sami Ghordoyee Milan, Naser Arya Azar, and Ronny Berndtsson. "A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level." Water 14, no. 5 (February 26, 2022): 751. http://dx.doi.org/10.3390/w14050751.
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Groundwater is a crucial source of water supply in drought conditions, and an auxiliary water source in wet seasons. Due to its increasing importance in view of climate change, predicting groundwater level (GWL) needs to be improved to enhance management. We used adaptive neuro-fuzzy inference systems (ANFIS) to predict the GWL of the Urmia aquifer in northwestern Iran under various input scenarios using precipitation, temperature, groundwater withdrawal, GWL during the previous month, and river flow. In total, 11 input patterns from various combinations of variables were developed. About 70% of the data were used to train the models, while the rest were used for validation. In a second step, several metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were used to improve the model and, consequently, prediction performance. The results showed that (i) RMSE, MAPE, and NSE of 0.51 m, 0.00037 m, and 0.86, respectively, were obtained for the ANFIS model using all input variables, indicating a rather poor performance, (ii) metaheuristic algorithms were able to optimize the parameters of the ANFIS model in predicting GWL, (iii) the input pattern that included all input variables resulted in the most appropriate performance with RMSE, MAPE, and NSE of 0.28 m, 0.00019 m, and 0.97, respectively, using the ANIFS-ACOR hybrid model, (iv) results of Taylor’s diagram (CC = 0.98, STD = 0.2, and RMSD = 0.30), as well as the scatterplot (R2 = 0.97), showed that best prediction was achieved by ANFIS-ACOR, and (v) temperature and evaporation exerted stronger influence on GWL prediction than groundwater withdrawal and precipitation. The findings of this study reveal that metaheuristic algorithms can significantly improve the performance of the ANFIS model in predicting GWL.
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Mindit Eriyadi, S.Pd, M.T. "PERANCANGAN DAN SIMULASI BASIC ENGINE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)." TEMATIK 2, no. 2 (December 30, 2015): 105–13. http://dx.doi.org/10.38204/tematik.v2i2.76.
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Abstrak : Adaptif Neuro Fuzzy Inference System ( ANFIS ) merupakan salah satu variasi bentuk dari fuzzy. Untuk dapat menggunakan ANFIS, dapat dibuat engine ANFIS yang berfungsi menjalankan logika fuzzy yang dirancang. Perancangan dan simulasi basic engine ANFIS ini bertujuan untuk merancang sebuah basic engine ANFIS dan menguji performansinya dalam sebuah simulasi. Perancangan dan pengujian simulasi dilakukan dengan menggunakan perangkat lunak MATLAB 7.5.0 dengan fitur anfis editor. Dari hasil pengujian simulasi basic engine ANFIS yang dirancang, didapatkan hasil bahwa basic engine yang dirancang dapat menghasikan sebuah keputusan yang tepat dengan menggunakan input – input yang ada.
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Alajmi, Mahdi S., and Abdullah M. Almeshal. "Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method." Materials 13, no. 13 (July 4, 2020): 2986. http://dx.doi.org/10.3390/ma13132986.
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This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.
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Chairi, Rayjansof, Fitria Hidayanti, and Idris Kusuma. "Perancangan Sistem Kendali Cascade pada Deaerator Berbasis Adaptive Neuro Fuzzy Inference System (ANFIS)." Jurnal Ilmiah Giga 20, no. 1 (March 20, 2019): 22. http://dx.doi.org/10.47313/jig.v20i1.548.
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Pada penelitian ini dilakukan perancangan pengendali cascade dengan kombinasi<br />ANFIS – ANFIS untuk diimplementasikan pada pengendalian proses. Objek yang<br />digunakan pada penelitian ini adalah deaerator pada pengindentifikasian dan Pressure-rig<br />38-714 pada pengujian respon yang keduanya mendukung konfigurasi cascade. Pada<br />pengindentifikasian menggunakan Adaptif Neuro Fuzzy Inference System (ANFIS), variabel<br />yang dikendalikan pada siklus utama adalah level pada Dearator sedangkan pada siklus<br />sekundernya adalah laju aliran. Pada pengujian respon, variabel yang dikendalikan pada<br />siklus utama adalah tekanan, sedangkan pada siklus sekunder adalah laju aliran. Metode<br />yang diajukan adalah dengan mengganti kombinasi pengendali pada arsitektur cascade<br />dengan menggunakan ANFIS – ANFIS untuk meningkatkan performa pengendalian.<br />Perbandingan dilakukan pada kombinasi PID – PID, ANFIS – PID, dan ANFIS – ANFIS.<br />ANFIS – ANFIS menghasilkan pengendalian lebih baik dengan maksimum overshoot, rise<br />time, dan settling time berturut – turut adalah tidak ada overshoot, 7 s, dan 10 s. sedangkan<br />pada PID – PID dan ANFIS – PID berturut – turut, 22% dan 4 %, 6.05 s, 35.5 s dan 10 s.
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Ikram, Rana Muhammad Adnan, Xinyi Cao, Tayeb Sadeghifar, Alban Kuriqi, Ozgur Kisi, and Shamsuddin Shahid. "Improving Significant Wave Height Prediction Using a Neuro-Fuzzy Approach and Marine Predators Algorithm." Journal of Marine Science and Engineering 11, no. 6 (June 1, 2023): 1163. http://dx.doi.org/10.3390/jmse11061163.
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This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate adaptive regression spline was investigated in deciding the best input for prediction models. The ANFIS-MPA model generally offered better accuracy than the other hybrid models in predicting significant wave height in both stations. It improved the accuracy of ANFIS-PSO and ANFIS-GA by 8.3% and 11.2% in root mean square errors in predicting a 1 h lead time in the test period.
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Ehteram, Mohammad, Haitham Abdulmohsin Afan, Mojgan Dianatikhah, Ali Najah Ahmed, Chow Ming Fai, Md Shabbir Hossain, Mohammed Falah Allawi, and Ahmed Elshafie. "Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors." Water 11, no. 6 (May 29, 2019): 1130. http://dx.doi.org/10.3390/w11061130.
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The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons.
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Çubukçu, Esra Aslı, Esra Uray, and Vahdettin Demir. "Fuzzy logic based prediction of retaining wall stability." Challenge Journal of Structural Mechanics 9, no. 4 (December 18, 2023): 145. http://dx.doi.org/10.20528/cjsmec.2023.04.003.
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In geotechnical engineering, retaining walls are widely employed to solve the problem of supporting horizontal loads occurring between two different soil levels. In the traditional retaining wall design, stability checks continue until a safe design is obtained according to selected wall dimensions and soil properties. This design method is a process that is time-consuming and based on trial and error. In this study, the stability control of the retaining wall, which is a complex engineering design, has been carried out with fuzzy logic methods. Adaptive network-based fuzzy inference systems (ANFISs) including Grid Partition (ANFIS-GP) and Substructive Clustering (ANFIS-SC) have been utilized as fuzzy logic methods. The sliding stability criterion of the cantilever retaining wall has been obtained by performing 1024 retaining wall designs which are created using different wall dimensions. Ninety percent and ten percent of the 1024 sliding safety factor values acquired through numerical analyses were respectively allocated to the training and testing phases. The prediction performances of the methods have been evaluated by considering the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) obtained for the sliding safety factors during the training and testing stages. Upon juxtaposing the actual and anticipated sliding safety factors for a dataset comprising 1024 observations, it has become evident that the ANFIS-SC methodology outperforms the ANFIS-GP approach in terms of predictive accuracy. Furthermore, this analysis culminated in the determination that the application of fuzzy logic methods stands as an efficacious and dependable means for checking the stability criteria of retaining walls.
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Genc, Onur, Ozgur Kisi, and Mehmet Ardiclioglu. "Modeling velocity distributions in small streams using different neuro-fuzzy and neural computing techniques." Journal of Water and Climate Change 11, no. 2 (January 29, 2019): 390–401. http://dx.doi.org/10.2166/wcc.2019.103.
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Abstract Accurate estimation of velocity distribution in open channels or streams (especially in turbulent flow conditions) is very important and its measurement is very difficult because of spatio-temporal variation in velocity vectors. In the present study, velocity distribution in streams was estimated by two different artificial neural networks (ANN), ANN with conjugate gradient (ANN-CG) and ANN with Levenberg–Marquardt (ANN-LM), and two different adaptive neuro-fuzzy inference systems (ANFIS), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC). The performance of the proposed models was compared with the multiple-linear regression (MLR) model. The comparison results revealed that the ANN-CG, ANN-LM, ANFIS-GP, and ANFIS-SC models performed better than the MLR model in estimating velocity distribution. Among the soft computing methods, the ANFIS-GP was observed to be better than the ANN-CG, ANN-LM, and ANFIS-SC models. The root mean square errors (RMSE) and mean absolute errors (MAE) of the MLR model were reduced by 69% and 72%, respectively, using the ANFIS-GP model to estimate velocity distribution in the test period.
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Roy, Dilip, Sujit Biswas, Mohamed Mattar, Ahmed El-Shafei, Khandakar Murad, Kowshik Saha, Bithin Datta, and Ahmed Dewidar. "Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models." Water 13, no. 21 (November 6, 2021): 3130. http://dx.doi.org/10.3390/w13213130.
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Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one- and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the individual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based individual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HA-ANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and individual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all individual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs.
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Liu, Peilin, Wenhao Leng, and Wei Fang. "Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/595639.
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This paper proposes a novel method of training the parameters of adaptive-network-based fuzzy inference system (ANFIS). Different from the previous works which emphasized on gradient descent (GD) method, we present an approach to train the parameters of ANFIS by using an improved version of quantum-behaved particle swarm optimization (QPSO). This novel variant of QPSO employs an adaptive dynamical controlling method for the contraction-expansion (CE) coefficient which is the most influential algorithmic parameter for the performance of the QPSO algorithm. The ANFIS trained by the proposed QPSO with adaptive dynamical CE coefficient (QPSO-ADCEC) is applied to five example systems. The simulation results show that the ANFIS-QPSO-ADCEC method performs much better than the original ANFIS, ANFIS-PSO, and ANFIS-QPSO methods.
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Khaled, Belouz, Aidaoui Abdellah, Dechemi Noureddine, Heddam Salim, and Aguenini Sabeha. "Modelling of biochemical oxygen demand from limited water quality variable by ANFIS using two partition methods." Water Quality Research Journal 53, no. 1 (December 30, 2017): 24–40. http://dx.doi.org/10.2166/wqrj.2017.015.
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Abstract This paper aims to: (1) develop models based on adaptive neuro-fuzzy inference system (ANFIS) able to predict five-day biochemical oxygen demand (BOD5) in Ouizert reservoir; (2) demonstrate the capability of the ANFIS in the practical issues of water quality management; (3) choose the optimal combination of input variables to improve the model performance; (4) compare two ANFIS partition methods, namely subtractive clustering called ANFIS-SC and grid partitioning, called ANFIS-GP. The models were developed using experimental data which were gathered during a ten-year period, at a mean monthly time step (scale). The input data used are total inorganic nitrogen, chemical oxygen demand (COD), total dissolved solid, dissolved oxygen and phosphate; the output is five-day biochemical oxygen demand (BOD5). Results reveal that ANFIS-SC models gave a higher correlation coefficient, a lower root mean square errors (RMSE) and mean absolute errors than the corresponding ANFIS-GP models. We can conclude that ANFIS-SC has supremacy over ANFIS-GP in terms of performance criteria and prediction accuracy for BOD5 estimation. The results showed that COD is the more effective variable for BOD5 estimating than other parameters, hence COD is the major driving factor for BOD5 modelling through ANFIS.
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Ramesh, K., A. P. Kesarkar, J. Bhate, M. Venkat Ratnam, and A. Jayaraman. "Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations." Atmospheric Measurement Techniques 8, no. 1 (January 22, 2015): 369–84. http://dx.doi.org/10.5194/amt-8-369-2015.
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Abstract. The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.
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Belvederesi, Chiara, John A. Dominic, Quazi K. Hassan, Anil Gupta, and Gopal Achari. "Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System." Water 12, no. 6 (June 6, 2020): 1622. http://dx.doi.org/10.3390/w12061622.
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Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. This study proposes a novel technique to forecast flows that use a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) along the Athabasca River in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, gauging data measured near the source were used to predict river flow near the mouth, over approximately 1000 km. The performance of this technique was compared to nonsequential and multi-input ANFISs, which use gauging data measured at each of the four hydrometric stations. The results show that a sequential ANFIS can accurately predict river flow (r2 = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) by using a single input, compared to nonsequential and multi-input ANFIS (2 days). This method provides accurate predictions over large distances, allowing for flow forecasts over longer periods of time. Therefore, governmental agencies and community planners could utilize this technique to improve flood prevention and planning, operations, maintenance, and the administration of water resource systems.
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AlRassas, Ayman Mutahar, Mohammed A. A. Al-qaness, Ahmed A. Ewees, Shaoran Ren, Mohamed Abd Elaziz, Robertas Damaševičius, and Tomas Krilavičius. "Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting." Processes 9, no. 7 (July 9, 2021): 1194. http://dx.doi.org/10.3390/pr9071194.
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Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.
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Yaseen, Zaher, Isa Ebtehaj, Sungwon Kim, Hadi Sanikhani, H. Asadi, Mazen Ghareb, Hossein Bonakdari, Wan Wan Mohtar, Nadhir Al-Ansari, and Shamsuddin Shahid. "Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis." Water 11, no. 3 (March 10, 2019): 502. http://dx.doi.org/10.3390/w11030502.
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In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the effect of monthly, seasonal, and annual history were considered. The performances of the forecasting models were assessed using various forecasting skill indicators. Moreover, an uncertainty analysis of the developed forecasting models was performed to evaluate the ability of the hybrid ANFIS models. The bound width of 95% confidence interval (d-factor) and the percentage of observed samples which was enveloped by 95% forecasted uncertainties (95PPU) were used for this purpose. The results indicated that all the hybrid ANFIS models performed better than the conventional ANFIS and for all input combinations. The obtained results showed that the models with best input combinations had the (95PPU and d-factor) values of (91.67 and 1.41), (91.03 and 1.41), (89.74 and 1.42), and (88.46 and 1.43) for ANFIS-PSO, ANFIS-GA, ANFIS-DE, and the conventional ANFIS, respectively. Based on the 95PPU and d-factor, it is concluded that all hybrid ANFIS models have an acceptable degree of uncertainty in forecasting monthly rainfall. The results of this study proved that the hybrid ANFIS with an evolutionary algorithm is a reliable modeling technique for forecasting monthly rainfall.
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Srisaeng, Panarat, and Glenn Baxter. "Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach." Transport and Telecommunication Journal 23, no. 2 (April 1, 2022): 151–59. http://dx.doi.org/10.2478/ttj-2022-0013.
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Abstract The forecasting of future airline passenger demand is critical task for airline management. The objective of the present study was to develop an adaptive neuro-fuzzy inference system (ANFIS) for predicting Australia’s domestic airline passenger demand. The ANFIS model was trained, tested, and validated in the study. Sugeno fuzzy rules were used in the ANFIS structure and Gaussian membership function, and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. The results found that the mean absolute percentage error (MAPE) for the overall data set of the ANFIS model was 3.25% demonstrating that the ANFIS model has high predictive capabilities. The ANFIS model could be used in other domestic air travel markets.
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Nath, Amitabha, Fisokuhle Mthethwa, and Goutam Saha. "Runoff estimation using modified adaptive neuro-fuzzy inference system." Environmental Engineering Research 25, no. 4 (August 22, 2019): 545–53. http://dx.doi.org/10.4491/eer.2019.166.
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Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.
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Baffoe, Peter, and Cynthia Boye. "A Hybrid Intelligent Noise Pollution Prediction Model Based on ANFIS and Nature-Inspired Algorithms." American Journal of Mathematical and Computer Modelling 9, no. 1 (May 17, 2024): 9–21. http://dx.doi.org/10.11648/j.ajmcm.20240901.12.
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In developing countries, researches in the areas of epidemiology, urban planning and environmental issues, it is extremely difficult to predict urban noise level in the neighborhoods. The majority of the noise-predicting algorithms in use today have limitations when it comes to prediction of noise level changes during intra-urban development and hence, the resulting noise pollution. Two hybrid noise prediction models, including ANFIS and PSO; and ANFIS and GA, were developed for Tarkwa Nsuaem Municipality and their performances were evaluated by applying statistical indicators. These hybrids were created to supplement and improve ANFIS's shortcomings based on their respective strengths and capabilities. To compare the performances of the models, statistical indicators were used; ANFIS-PSO performed better than the ANFIS-GA. The indications show the disparities, with the RMSE of ANFIS-PSO being 0.8789 and that of ANFIS-GA being 1.0529. Moreover, the Standard Deviation and Mean Square Error of ANFIS-PSO are 0.8898 and 0.7725 respectively, then those of ANFIS-GA are 1.0660 and 1.1086 respectively. A map showing the distribution of the predicted noise levels was produced from the outcome of the ANFIS-PSO model. Comparing the predicted noise levels to the EPA standards, it was observed that there is a danger which means people living in that area with noise levels above 65 dB are at high risk of health effects.
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Ashokkumar, V., and C. B. Venkatramanan. "PSO-ANFIS-Based Energy Management in Hybrid AC/DC Microgrid along with Plugin Electric Vehicle." International Journal of Photoenergy 2023 (October 31, 2023): 1–24. http://dx.doi.org/10.1155/2023/2852972.
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This study proposes a hybrid AC/DC microgrid with plugin EVs, leveraging PSO-tuned ANFIS for voltage and power control. With the existing control, which faced challenges such as instability and complexity, the proposed approach is aimed at simplifying control through PSO, efficient power sharing, and reduced sample requirements. This innovative method contributes to improved energy management in hybrid microgrids, bridging existing research gaps. This approach streamlines neural transmission in microgrid control, addressing challenges in distributed generation power, load demand, energy storage system SOC, and AC grid power integration. Notably, the proposed PSO-ANFIS simplifies electric vehicle power references using distinct inputs for each mode, trained through PSO. This methodology is tailored for microgrids with varying power profiles, presenting a promising solution for efficient energy management. The proposed EMS was experimentally verified using MATLAB simulations of a small-scale hybrid AC/DC microgrid for every operating mode. The financial dynamics of a microgrid’s power exchange with the main grid are examined through three distinct methodologies: fuzzy logic, ANFIS (adaptive neurofuzzy inference system), and PSO-ANFIS (ANFIS optimized using particle swarm optimization). In case 1, the PSO-ANFIS approach demonstrates its superiority by achieving the lowest grid purchase power cost of 1995.24 Rs/day compared to fuzzy (2243.63 Rs/day) and ANFIS (2150.45 Rs/day), while also yielding the highest revenue from power selling to the microgrid: PSO-ANFIS (668.84 Rs/day) surpassing fuzzy (536.12 Rs/day) and ANFIS (575.35 Rs/day). Similarly, in case 2, PSO-ANFIS proves its efficiency with the lowest net price of 8619.192 Rs/day, showcasing its effectiveness in optimizing financial dynamics. Furthermore, in case 3, the revenue aligns precisely with net prices, indicating the PSO-ANFIS method’s financial advantage, generating the highest revenue of 6544.0224 Rs/day compared to fuzzy (6025.36 Rs/day) and ANFIS (6153.214 Rs/day). These findings underscore the potential utility of the PSO-ANFIS approach in optimizing microgrid operations and enhancing cost-effectiveness across various scenarios.
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Beniwal, Ruby, and Shruti Kalra. "ANFIS Based Thermal Estimation of Ultradeep Submicron Digital Circuit Design." Journal of Integrated Circuits and Systems 16, no. 3 (February 7, 2022): 1–10. http://dx.doi.org/10.29292/jics.v16i3.507.
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In this paper, the use of the Adaptive Neuro Fuzzy Inference System (ANFIS) to model the CMOS inverter is discussed as a tool for developing and simulating CMOS logic circuits at the ultradeep submicron technology node of 22nm. The ANFIS structures are built and trained using MATLAB software. The ANFIS network was trained using data obtained from the analytical model (at 298.15K and 398.15K). For training, two methodologies are used: a hybrid learning method based on back-propagation and least-squares estimation, and back-propagation. The effect of the ANFIS model's structure on the accuracy and performance of the CMOS inverter has also been investigated. Further, simulation through HSPICE using (Predictive Technology Model) PTM nominal parameters has been done to compare with ANFIS (trained using an analytical model) results. The comparison of ANFIS and HSPICE suggests the ANFIS modelling procedure's practicality and correctness. The findings demonstrate that the ANFIS simulation is significantly faster and more comparable than the HSPICE simulation and that it can be easily integrated into software tools for designing and simulating complicated CMOS logic circuits.
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Nikulin, Vyacheslav. "Modification of the Anfis network." Bulletin of Perm National Research Polytechnic University. Electrotechnics, Informational Technologies, Control Systems, no. 42 (September 12, 2022): 178–93. http://dx.doi.org/10.15593/2224-9397/2022.2.09.
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Rosyid, Abdur, Mohanad Alata, and Mohamed El Madany. "Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System." International Letters of Chemistry, Physics and Astronomy 55 (July 2015): 1–11. http://dx.doi.org/10.18052/www.scipress.com/ilcpa.55.1.
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This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.
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Rosyid, Abdur, Mohanad Alata, and Mohamed El Madany. "Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System." International Letters of Chemistry, Physics and Astronomy 55 (July 3, 2015): 1–11. http://dx.doi.org/10.56431/p-q1glae.
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This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.
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Shareef, Hussain, Saifulnizam Abd Khalid, Mohd Wazir Mustafa, and Azhar Khairuddin. "An ANFIS Approach for Real Power Transfer Allocation." Journal of Applied Mathematics 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/414258.
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This paper proposes an adaptive neurofuzzy interface system (ANFIS) approach to identify the real power transfer between generators. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to train the designed ANFIS. It also incorporated an enhanced feature extraction method called principle component analysis (PCA) to reduce the input features to the ANFIS. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the ANFIS output compared to that of the MNE method. The ANFIS output provides promising results in terms of accuracy and computation time. Furthermore, it can be concluded that the ANFIS with enhanced feature extraction method reduces the time taken to train the ANFIS without affecting the accuracy of the results.
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Chen, Wei, Haoyuan Hong, Mahdi Panahi, Himan Shahabi, Yi Wang, Ataollah Shirzadi, Saied Pirasteh, et al. "Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)." Applied Sciences 9, no. 18 (September 8, 2019): 3755. http://dx.doi.org/10.3390/app9183755.
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The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.
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Panoiu, Manuela, Caius Panoiu, and Sergiu Mezinescu. "Modelling and Prediction of Reactive Power at Railway Stations Using Adaptive Neuro Fuzzy Inference Systems." Applied Sciences 13, no. 1 (December 24, 2022): 212. http://dx.doi.org/10.3390/app13010212.
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Electricity has become an important concern in today’s society. This is due to the fact that the electric grid now has a greater number of non-linear components. The AC-powered locomotive is one of these non-linear components. The aim of this paper was to model and predict the reactive power produced by an AC locomotive. This paper presents a study on the modelling and prediction of reactive power produced by AC-powered electric locomotives. Reactive power flow has a significant impact on network voltage levels and power efficiency. The research was conducted by using intelligent techniques—more precisely, by using the adaptive neuro fuzzy inference system (ANFIS). Several approaches to the ANFIS structure were used in the research. Of these, we mention the ANFIS-grid partition, ANFIS subtractive clustering and ANFIS fuzzy c-means (FCM) clustering. Thus; for modelling and predicting reactive power, ANFIS was trained, then tested. For the training of ANFIS, experimental data obtained from measurements performed in a train supply sub-station were used. The measurements were taken over a period of time when the locomotives were far away from the station, close to the station, and at the station, respectively. The currents and voltages from the supply substation, respectively the active, reactive, and distorted powers, were measured on the data acquisition board. With the measured data of the reactive power, the modelling with ANFIS was performed, and a prediction of the variation in the reactive power was made. The paper analysed the results of the modelling by comparing between several types of ANFIS architectures. The values of RMSE, RMS and the training time of ANFIS were compared for several structures of ANFIS.
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Suparta, Wayan, and Kemal Maulana Alhasa. "Estimation of Atmospheric Water Vapor from ANFIS Technique and Its Validation with GPS Data." JURNAL INFOTEL 11, no. 1 (March 25, 2019): 8. http://dx.doi.org/10.20895/infotel.v11i1.426.
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Adaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between ANFIS and Global Positioning System (GPS) for areas in Pekan, Pahang, Malaysia. The PWV value was estimated with the ANFIS model with the surface meteorological data as inputs. The accuracy of PWV from ANFIS has been validated with PWV from GPS measurements for the period of 2010. The result showed that the ANFIS PWV has a similar trend with the GPS PWV (r = 0.999 at the 99% confidence level) and found a difference of 0.024%. The PWV from ANFIS was calculated 0.035% higher compared to GPS PWV and found a similar character in two seasonal monsoons. This indicates that the PWV obtained with ANFIS model agreed very well with GPS measurements and it can be implemented to monitor atmospheric variability as well as climate change studies in the absence of GPS data
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Salehi, R., and S. Chaiprapat. "Predicting H2S emission from gravity sewer using an adaptive neuro-fuzzy inference system." Water Quality Research Journal 57, no. 1 (December 8, 2021): 20–39. http://dx.doi.org/10.2166/wqrj.2021.018.
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Abstract A predictive model to estimate hydrogen sulfide (H2S) emission from sewers would offer engineers and asset managers the ability to evaluate the possible odor/corrosion problems during the design and operation of sewers to avoid in-sewer complications. This study aimed to model and forecast H2S emission from a gravity sewer, as a function of temperature and hydraulic conditions, without requiring prior knowledge of H2S emission mechanism. Two different adaptive neuro-fuzzy inference system (ANFIS) models using grid partitioning (GP) and subtractive clustering (SC) approaches were developed, validated, and tested. The ANFIS-GP model was constructed with two Gaussian membership functions for each input. For the development of the ANFIS-SC model, the MATLAB default values for clustering parameters were selected. Results clearly indicated that both the best ANFIS-GP and ANFIS-SC models produced smaller error compared with the multiple regression models and demonstrated a superior predictive performance on forecasting H2S emission with an excellent R2 value of >0.99. However, the ANFIS-GP model possessed fewer rules and parameters than the ANFIS-SC model. These findings validate the ANFIS-GP model as a potent tool for predicting H2S emission from gravity sewers.
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Fitriyati, Nina, Mahmudi Mahmudi, Madona Yunita Wijaya, and Maysun Maysun. "Forecasting Indonesian inflation using a hybrid ARIMA-ANFIS." Desimal: Jurnal Matematika 5, no. 3 (December 20, 2022): 289–304. http://dx.doi.org/10.24042/djm.v5i3.14093.
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This paper discusses the prediction of the inflation rate in Indonesia. The data used in this research is assumed to have both linear and non-linear components. The ARIMA model is selected to accommodate the linear component, while the ANFIS method accounts for the non-linear component in the inflation data. Thus, the model is known as the hybrid ARIMA-ANFIS model. The clustering method is performed in the ANFIS model using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 to 6 clusters. The optimal number of clusters is assessed according to the minimum value of the error prediction. To evaluate the performance of the fitted hybrid ARIMA-ANFIS model, it can be compared to the classical ARIMA model and with the ordinary ANFIS model. The result reveals that the best ARIMA model for inflation prediction in Indonesia is ARIMA(2,1,0). In the hybrid ARIMA(2,1,0)-ANFIS model, two clusters are optimal. Meanwhile, the optimum number of clusters in the ordinary ANFIS model is six. The comparison of prediction accuracy confirms that the hybrid model is superior to the individual model alone of either ARIMA or ANFIS model.
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Jasmine, Mansura, Abdolmajid Mohammadian, and Hossein Bonakdari. "On the Prediction of Evaporation in Arid Climate Using Machine Learning Model." Mathematical and Computational Applications 27, no. 2 (April 5, 2022): 32. http://dx.doi.org/10.3390/mca27020032.
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Evaporation calculations are important for the proper management of hydrological resources, such as reservoirs, lakes, and rivers. Data-driven approaches, such as adaptive neuro fuzzy inference, are getting popular in many hydrological fields. This paper investigates the effective implementation of artificial intelligence on the prediction of evaporation for agricultural area. In particular, it presents the adaptive neuro fuzzy inference system (ANFIS) and hybridization of ANFIS with three optimizers, which include the genetic algorithm (GA), firefly algorithm (FFA), and particle swarm optimizer (PSO). Six different measured weather variables are taken for the proposed modelling approach, including the maximum, minimum, and average air temperature, sunshine hours, wind speed, and relative humidity of a given location. Models are separately calibrated with a total of 86 data points over an eight-year period, from 2010 to 2017, at the specified station, located in Arizona, United States of America. Farming lands and humid climates are the reason for choosing this location. Ten statistical indices are calculated to find the best fit model. Comparisons shows that ANFIS and ANFIS–PSO are slightly better than ANFIS–FFA and ANFIS–GA. Though the hybrid ANFIS–PSO (R2= 0.99, VAF = 98.85, RMSE = 9.73, SI = 0.05) is very close to the ANFIS (R2 = 0.99, VAF = 99.04, RMSE = 8.92, SI = 0.05) model, preference can be given to ANFIS, due to its simplicity and easy operation.
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Wang, Yuzhen, Mohammad Rezaei, Rini Asnida Abdullah, and Mahdi Hasanipanah. "Developing Two Hybrid Algorithms for Predicting the Elastic Modulus of Intact Rocks." Sustainability 15, no. 5 (February 26, 2023): 4230. http://dx.doi.org/10.3390/su15054230.
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In the primary and final designs of projects related to rock mechanics and engineering geology, one of the key parameters that needs to be taken into account is the intact rock elastic modulus (E). To measure this parameter in a laboratory setting, core samples with high-quality and costly tools are required, which also makes for a time-consuming process. The aim of this study is to assess the effectiveness of two meta-heuristic-driven approaches to predicting E. The models proposed in this paper, which are based on integrated expert systems, hybridize the adaptive neuro-fuzzy inference system (ANFIS) with two optimization algorithms, i.e., the differential evolution (DE) and the firefly algorithm (FA). The performance quality of both ANFIS-DE and ANFIS-FA models was then evaluated by comparing them with ANFIS and neural network (NN) models. The ANFIS-DE and ANFIS-FA models were formed on the basis of the data collected from the Azad and Bakhtiari dam sites in Iran. After applying several statistical criteria, such as root mean square error (RMSE), the ANFIS-FA model was found superior to the ANFIS-DE, ANFIS, and NN models in terms of predicting the E value. Additionally, the sensitivity analysis results showed that the P-wave velocity further influenced E compared with the other independent variables.
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Nguyen, Hoang-Long, Binh Thai Pham, Le Hoang Son, Nguyen Trung Thang, Hai-Bang Ly, Tien-Thinh Le, Lanh Si Ho, Thanh-Hai Le, and Dieu Tien Bui. "Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction." Applied Sciences 9, no. 21 (November 5, 2019): 4715. http://dx.doi.org/10.3390/app9214715.
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The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.
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Raheema, Mithaq Nama, and Ahmad Shaker Abdullah. "Design of Prediction System for Aircraft's Position Based on Inverse Control Technique Using Adaptive Neuro-Fuzzy Interference System (ANFIS)." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (April 1, 2019): 238–48. http://dx.doi.org/10.29196/jubpas.v27i1.2117.
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This paper proposes the Adaptive Neuro-Fuzzy Interference System (ANFIS) method to realize the track correlation of Radar. ANFIS is used for the first time in inverse model in addition to model of aircraft position radar from the recorded data. The simulation results show that the proposed ANFIS controller has been successfully implemented. Root mean square error is applied to measure the performance of ANFIS that revealed the optimal setting needed for better estimation of the aircraft position. Results with RMSE less than 10-4 also show that the controller with ANFIS yields good tracking performance, valuable and easy to implement.
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Li, Yuheng, Shuxing Xu, Zhaofei Fan, Xiao Zhang, Xiaohui Yang, Shuo Wen, and Zhongjie Shi. "Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area." Remote Sensing 15, no. 1 (December 22, 2022): 42. http://dx.doi.org/10.3390/rs15010042.
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Wildfire is essential in altering land ecosystems’ structures, processes, and functions. As a critical disturbance in the China–Mongolia–Russia cross-border area, it is vital to understand the potential drivers of wildfires and predict where wildfires are more likely to occur. This study assessed factors affecting wildfire using the Random Forest (RF) model. No single factor played a decisive role in the incidence of wildfires. However, the climatic variables were most critical, dominating the occurrence of wildfires. The probability of wildfire occurrence was simulated and predicted using the Adaptive Network-based Fuzzy Inference System (ANFIS). The particle swarm optimization (PSO) model and genetic algorithm (GA) were used to optimize the ANFIS model. The hybrid ANFIS models performed better than single ANFIS for the training and validation datasets. The hybrid ANFIS models, such as PSO-ANFIS and GA-ANFIS, overcome the over-fitting problem of the single ANFIS model at the learning stage of the wildfire pattern. The high classification accuracy and good model performance suggest that PSO-ANFIS can be used to predict the probability of wildfire occurrence. The probability map illustrates that high-risk areas are mainly distributed in the northeast part of the study area, especially the grassland and forest area of Dornod Province of Mongolia, Buryatia, and Chita state of Russia, and the northeast part of Inner Mongolia, China. The findings can be used as reliable estimates of the relative likelihood of wildfire hazards for wildfire management in the region covered or vicinity.
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Diarsih, Inas Husna, Tarno Tarno, and Agus Rusgiyono. "PEMODELAN PRODUKSI BAWANG MERAH DI JAWA TENGAH DENGAN MENGGUNAKAN HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE – ADAPTIVE NEURO FUZZY INFERENCE SYSTEM." Jurnal Gaussian 7, no. 3 (August 29, 2018): 281–92. http://dx.doi.org/10.14710/j.gauss.v7i3.26661.
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Red onion is one of the strategic horticulture commodities in Indonesia considering its function as the main ingredients of the basic ingredients of Indonesian cuisine. In an effort to increase production to supply national necessary, Central Java as the main center of red onion production should be able to predict the production of several periods ahead to maintain the balance of national production. The purpose of this research is to get the best model to forecast the production of red onion in Central Java by ARIMA, ANFIS, and hybrid ARIMA-ANFIS method. Model accuracy is measured by the smallest RMSE and AIC values. The results show that the best model to modeling red onion production in Central Java is obtained by hybrid ARIMA-ANFIS model which is a combination between SARIMA ([2], 1, [12]) and residual ARIMA using ANFIS model with input et,1, et,2 on the grid partition technique, gbell membership function, and membership number of 2 that produce RMSE 12033 and AIC 21.6634. While ARIMA model yield RMSE 13301,24 and AIC 21,89807 with violation of assumption. And the ANFIS model produces RMSE 14832 and AIC 22,0777. This shows that ARIMA-ANFIS hybrid method is better than ARIMA and ANFIS.Keywords: production of red onion, ARIMA, ANFIS, hybrid ARIMA-ANFIS
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Kitano, Houichi, and Terumi Nakamura. "Predicting Residual Weld Stress Distribution with an Adaptive Neuro-Fuzzy Inference System." International Journal of Automation Technology 12, no. 3 (May 1, 2018): 290–96. http://dx.doi.org/10.20965/ijat.2018.p0290.
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This work is an investigation into the applicability of the adaptive neuro-fuzzy inference system (ANFIS), a machine learning technique, to develop a model of the relation of residual stress distribution in a single weld bead-on-plate part to weld heat input and distance from the center of the weld line. Residual stress distributions required to train the ANFIS model were obtained through thermal elastic-plastic finite element analysis. Appropriate conditions for training the ANFIS model were investigated by evaluating the prediction error of the ANFIS model developed under various conditions. Afterward, residual stress distributions obtained by the developed ANFIS model trained under the appropriate conditions were compared with those obtained through thermal elastic-plastic finite element analysis. Discrepancies between the residual stresses obtained through the ANFIS model and thermal elastic-plastic finite element analysis were smaller than ±40 MPa in all regions. The results suggest that the ANFIS modeling had the ability to learn and generalize residual weld stress distributions in single weld bead-on-plate parts.
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FAKHRUDDIN, HANIF HASYIER, HANDRI TOAR, ERA PURWANTO, HARY OKTAVIANTO, GAMAR BASUKI, RADEN AKBAR NUR APRIYANTO, and ABDILLAH AZIZ MUNTASHIR. "Strategi Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) pada Kendali Motor Induksi 3 Fase Metode Vektor-Tidak Langsung." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 9, no. 4 (October 10, 2021): 786. http://dx.doi.org/10.26760/elkomika.v9i4.786.
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ABSTRAKKendali vektor merupakan solusi terbaik dalam kendali motor induksi untuk meningkatkan karakter dinamis dan efisiensinya. Pada penelitian ini, sebuah kendali kecepatan PID dipadukan dengan Adaptive Neuro Fuzzy Inference System (ANFIS) untuk meningkatkan keandalan pada berbagai kecepatan acuan. Metode cerdas Particle Swarm Optimization (PSO) digunakan untuk optimasi dataset ANFIS. Pengujian keandalan dilakukan dengan membandingkan PID konvensional dengan PID-ANFIS pada motor induksi 3 fase berdaya 2HP. Validasi penelitian dilakukan melalui simulasi di platform LabView. PID-ANFIS membuktikan hasil yang jauh lebih baik dari kendali PID konvensional pada berbagai kecepatan acuan. Pemilihan rise time tercepat sebagai fungsi fitness menghasilkan kendali yang memiliki dead time dan rise time 1.5x lebih cepat. PID-ANFIS berhasil menghilangkan undershoot dan osilasi steady state ketika uji kecepatan tinggi.Kata kunci: Kendali Vektor, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView ABSTRACTVector control is the best solution in induction motor control to enhance its dynamic character and efficiency. In this research, a PID speed controller is combined with the Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance reliability at various reference speeds. The intelligent method Particle Swarm Optimization (PSO) is used to optimize the ANFIS dataset. Reliability testing is done by comparing conventional PID with PID-ANFIS on a 2HP 3-phase induction motor. The research validation was carried out through a simulation on the LabView platform. The PID-ANFIS proved significantly better results than conventional PID control at a wide range of reference speeds. Selection of the fastest rise time as a fitness function results in a control that has a dead time and a rise time of 1.5x faster. PID-ANFIS successfully negates undershoot and steadystate oscillations during high-speed tests.Keywords: Vector Control, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView
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AlAyyash, Saad, A’kif Al-Fugara, Rania Shatnawi, Abdel Rahman Al-Shabeeb, Rida Al-Adamat, and Hani Al-Amoush. "Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping." Sustainability 15, no. 3 (January 30, 2023): 2499. http://dx.doi.org/10.3390/su15032499.
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The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms results in six models: SVM–GA, SVM–IWO, SVM–TLBO, ANFIS–GA, ANFIS–IWO, and ANFIS–TLBO. A database consisting of well data containing 464 wells with 12 predictive factors was developed for the groundwater potential mapping (GPM) of the study area. Of the 464 well locations, 70% (325 locations) were assigned for the training set and the rest (139 locations) for the validation set. The correlation between the 12 predictive factors and the well locations is analyzed using the frequency ratio (FR) statistical model. An area under receiver operating characteristic (AUROC) curve was used to evaluate and compare the models. According to the results, the SVM-based hybrid models outperformed other ANFIS hybrid models in the learning (training) and validation phases. The SVM–GA and SVM–TLBO hybrid models showed AUROC values of 0.984 and 0.971, respectively, in the training and validation phases. Moreover, the ANFIS–GA and ANFIS–TLBO hybrid models showed an AUROC of 0.979 and 0.984 in the training phase and an AUROC of 0.973 and 0.984 in the validation phase, respectively. The SVM–IWO and ANFIS–IWO hybrid models showed the lowest AUROC. This study demonstrated the more efficient results of the SVM-based hybrid models in comparison with the ANFIS-based hybrid models in terms of accuracy and modeling speed.