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Journal articles on the topic 'Fuzzy GMDH'

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Published: 30 May 2022
Last updated: 31 May 2022

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1

Ozovehe, Aliyu, Okpo U. Okereke, Anene E. Chibuzo, and Abraham U. Usman. "Comparative Analysis of Traffic Congestion Prediction Models for Cellular Mobile Macrocells." European Journal of Engineering Research and Science 3, no. 6 (June 30, 2018): 32. http://dx.doi.org/10.24018/ejers.2018.3.6.767.

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Traffic congestion prediction is a non-linear process that involves obtaining valuable information from a set of traffic data and regression or auto-regression linear models cannot be applied as they are limited in their ability to deal with such problems. However, Artificial Intelligent (AI) techniques have shown great ability to deal with non-linear problems and two of such techniques which have found application in traffic prediction are the Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). In this work, Multiple Layer Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Network (RBF-NN), Group Method of Data Handling (GMDH) and an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are trained based on busy hour (BH) traffic measurement data taken from some GSM/GPRS sites in Abuja, Nigeria. The trained networks were then used to predict traffic congestion for some macrocells and their accuracy are compared using four statistical indices. The GMDH model on the average gave goodness of fit (R2), root mean square error (RMSE), standard deviation (σ), and mean absolute error (µ) values of 99, 3.16, 3.53 and 2.32 % respectively. It was observed that GMDH model has the best fit in all cases and on the average predict better than ANFIS, MLP and RBF models. The GMDH model is found to offer improved prediction results in terms of increasing the R2 by 20% and reducing RMSE by 60% over ANFIS, the closest model to the GMDH in term of prediction accuracy.
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2

Ozovehe, Aliyu, Okpo U. Okereke, Anene E. Chibuzo, and Abraham U. Usman. "Comparative Analysis of Traffic Congestion Prediction Models for Cellular Mobile Macrocells." European Journal of Engineering and Technology Research 3, no. 6 (June 30, 2018): 32–38. http://dx.doi.org/10.24018/ejeng.2018.3.6.767.

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Traffic congestion prediction is a non-linear process that involves obtaining valuable information from a set of traffic data and regression or auto-regression linear models cannot be applied as they are limited in their ability to deal with such problems. However, Artificial Intelligent (AI) techniques have shown great ability to deal with non-linear problems and two of such techniques which have found application in traffic prediction are the Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). In this work, Multiple Layer Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Network (RBF-NN), Group Method of Data Handling (GMDH) and an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are trained based on busy hour (BH) traffic measurement data taken from some GSM/GPRS sites in Abuja, Nigeria. The trained networks were then used to predict traffic congestion for some macrocells and their accuracy are compared using four statistical indices. The GMDH model on the average gave goodness of fit (R2), root mean square error (RMSE), standard deviation (?), and mean absolute error (µ) values of 99, 3.16, 3.53 and 2.32 % respectively. It was observed that GMDH model has the best fit in all cases and on the average predict better than ANFIS, MLP and RBF models. The GMDH model is found to offer improved prediction results in terms of increasing the R2 by 20% and reducing RMSE by 60% over ANFIS, the closest model to the GMDH in term of prediction accuracy.
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3

Zaychenko, Yuriy, and Helen Zaychenko. "Fuzzy GMDH and its application to forecasting financial processes." System research and information technologies, no. 1 (March 25, 2019): 91–109. http://dx.doi.org/10.20535/srit.2308-8893.2019.1.07.

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4

Mohanty, Ramakanta, V. Ravi, and M. R. Patra. "Application of Machine Learning Techniques to Predict Software Reliability." International Journal of Applied Evolutionary Computation 1, no. 3 (July 2010): 70–86. http://dx.doi.org/10.4018/jaec.2010070104.

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In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.
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5

Yousefpour, A., and Z. Ahmadpour. "The Prediction Of Air Pollution By Using Neuro-fuzzy Gmdh." Journal of Mathematics and Computer Science 02, no. 03 (April 15, 2011): 488–94. http://dx.doi.org/10.22436/jmcs.02.03.13.

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6

NAGASAKA, K., H. ICHIHASHI, and R. LEONARD. "Neuro-fuzzy GMDH and its application to modelling grinding characteristics." International Journal of Production Research 33, no. 5 (May 1995): 1229–40. http://dx.doi.org/10.1080/00207549508930206.

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7

Zhu, Bing, Chang-Zheng He, Panos Liatsis, and Xiao-Yu Li. "A GMDH-based fuzzy modeling approach for constructing TS model." Fuzzy Sets and Systems 189, no. 1 (February 2012): 19–29. http://dx.doi.org/10.1016/j.fss.2011.08.004.

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8

Hayashi, Isao, and Hideo Tanaka. "The fuzzy GMDH algorithm by possibility models and its application." Fuzzy Sets and Systems 36, no. 2 (June 1990): 245–58. http://dx.doi.org/10.1016/0165-0114(90)90182-6.

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9

Heydari, Azim, Meysam Majidi Nezhad, Mehdi Neshat, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli, and Lina Bertling Tjernberg. "A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data." Energies 14, no. 12 (June 11, 2021): 3459. http://dx.doi.org/10.3390/en14123459.

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A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. A combined K-means and identifying density-based local outliers is applied to detect and clean the outliers of the raw supervisory control and data acquisition data in the proposed forecasting model. Moreover, the empirical mode decomposition is employed to decompose signals and pre-processing data. The fuzzy GMDH neural network is a forecaster engine to estimate the future amount of wind turbines energy production, where the grey wolf optimization is used to optimize the fuzzy GMDH neural network parameters in order to achieve a lower forecasting error. Moreover, the model has been applied using actual data from a pilot onshore wind farm in Sweden. The obtained results indicate that the proposed model has a higher accuracy than others in the literature and provides single and combined forecasting models in different time-steps ahead and seasons.
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10

Harandizadeh, Hooman. "Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 1 (January 30, 2020): 114–26. http://dx.doi.org/10.1017/s0890060420000025.

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AbstractThis research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.
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11

Najafzadeh, Mohammad, and Ahmed M. A. Sattar. "Neuro-Fuzzy GMDH Approach to Predict Longitudinal Dispersion in Water Networks." Water Resources Management 29, no. 7 (March 14, 2015): 2205–19. http://dx.doi.org/10.1007/s11269-015-0936-8.

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12

YOKODE, Katsunori, Hideo TANAKA, and Hisao ISHIBUCHI. "Fuzzy If-Then Rules with Certainty Factors using Multilayer Model of GMDH." Journal of Japan Society for Fuzzy Theory and Systems 7, no. 1 (1995): 131–41. http://dx.doi.org/10.3156/jfuzzy.7.1_131.

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13

HAYASHI, Isao, Hideo TANAKA, Toshio OHONO, and Fusetsu TAKAGI. "Analysis and Prediction of Water Temperatures in a Reservior by the Fuzzy GMDH." Transactions of the Society of Instrument and Control Engineers 23, no. 12 (1987): 1304–11. http://dx.doi.org/10.9746/sicetr1965.23.1304.

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14

Najafzadeh, Mohammad, and Hazi Mohammad Azamathulla. "Neuro-Fuzzy GMDH to Predict the Scour Pile Groups due to Waves." Journal of Computing in Civil Engineering 29, no. 5 (September 2015): 04014068. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000376.

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15

Mahdavi-Meymand, Amin, Wojciech Sulisz, and Mohammad Zounemat-Kermani. "A comprehensive study on the application of firefly algorithm in prediction of energy dissipation on block ramps." Eksploatacja i Niezawodnosc - Maintenance and Reliability 24, no. 2 (February 26, 2022): 200–210. http://dx.doi.org/10.17531/ein.2022.2.2.

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In this study novel integrative machine learning models embedded with the firefly algorithm (FA) were developed and employed to predict energy dissipation on block ramps. The used models include multi-layer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), support vector regression (SVR), linear equation (LE), and nonlinear regression equation (NE). The investigation focused on the evaluation of the performance of standard and integrative models in different runs. The performances of machine learning models and the nonlinear equation are higher than the linear equation. The results also show that FA increases the performance of all applied models. Moreover, the results indicate that the ANFIS-FA is the most stable integrative model in comparison to the other embedded methods and reveal that GMDH and SVR are the most stable technique among all applied models. The results also show that the accuracy of the LE-FA technique is relatively low, RMSE=0.091. The most accurate results provide SVR-FA, RMSE=0.034.
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16

Najafzadeh, Mohammad, and Siow Yong Lim. "Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates." Earth Science Informatics 8, no. 1 (February 6, 2014): 187–96. http://dx.doi.org/10.1007/s12145-014-0144-8.

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17

Bodyanskiy, Yevgeniy V., Yuriy P. Zaychenko, Galib Hamidov, and Nonna Ye Kulishova. "Multilayer GMDH-neuro-fuzzy network based on extended neo-fuzzy neurons and its application in online facial expression recognition." System research and information technologies, no. 3 (December 7, 2020): 66–78. http://dx.doi.org/10.20535/srit.2308-8893.2020.3.05.

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18

OH, SUNG-KWUN, DONG-WON KIM, and WITOLD PEDRYCZ. "HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 03 (June 2002): 257–80. http://dx.doi.org/10.1142/s0218488502001478.

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We propose a hybrid architecture based on a combination of fuzzy systems and polynomial neural networks. The resulting Hybrid Fuzzy Polynomial Neural Networks (HFPNN) dwells on the ideas of fuzzy rule-based computing and polynomial neural networks. The structure of the network comprises of fuzzy polynomial neurons (FPNs) forming the nodes of the first (input) layer of the HFPNN and polynomial neurons (PNs) that are located in the consecutive layers of the network. In the FPN (that forms a fuzzy inference system), the generic rules assume the form "if A then y = P(x) " where A is fuzzy relation in the condition space while P(x) is a polynomial standing in the conclusion part of the rule. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as constant, linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are considered. Each PN of the network realizes a polynomial type of partial description (PD) of the mapping between input and out variables. HFPNN is a flexible neural architecture whose structure is based on the Group Method of Data Handling (GMDH) and developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. The experimental part of the study involves two representative numerical examples such as chaotic time series and Box-Jenkins gas furnace data.
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19

Nasir, Vahid, Sepideh Nourian, Stavros Avramidis, and Julie Cool. "Stress wave evaluation for predicting the properties of thermally modified wood using neuro-fuzzy and neural network modeling." Holzforschung 73, no. 9 (August 27, 2019): 827–38. http://dx.doi.org/10.1515/hf-2018-0289.

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AbstractThis study investigated using the stress wave method to predict the properties of thermally modified wood by means of an adaptive neuro-fuzzy inference system (ANFIS) and neural network (NN) modeling. The stress wave was detected using a pair of accelerometers and an acoustic emission (AE) sensor, and the effect of heat treatment (HT) on the physical and mechanical properties of wood as well as wave velocity and AE signal is discussed. The AE signal was processed in the time and time-frequency domains using wavelet analysis and different features were extracted for network training. The auto-associative NN is used as a dimensional reduction method to decrease the dimension of the extracted AE features and enhance the ANFIS performance. It was shown that while the stress wave velocity using the accelerometer did not result in an accurate model, the network performance significantly increased when trained with the AE features. The AE signal exhibited a significant correlation with wood treatment and porosity. The best ANFIS performance corresponded to predicting the wood swelling coefficient, equilibrium moisture content (EMC) and water absorption (WA), respectively. However, the AE signal did not seem suitable for predicting the wood density and hardness. The performance of ANFIS was compared with the “group method of data handling” (GMDH) NN. Both the ANFIS and GMDH networks showed higher accuracy than the multivariate linear regression (MVLR) model.
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20

Najafzadeh, Mohammad, and Abdolreza Zahiri. "Neuro-Fuzzy GMDH-Based Evolutionary Algorithms to Predict Flow Discharge in Straight Compound Channels." Journal of Hydrologic Engineering 20, no. 12 (December 2015): 04015035. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001185.

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21

Hwang, Heung Suk. "Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication." Computers & Industrial Engineering 50, no. 4 (August 2006): 450–57. http://dx.doi.org/10.1016/j.cie.2005.08.005.

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22

Naganna, Sujay Raghavendra, Beste Hamiye Beyaztas, Neeraj Bokde, and Asaad M. Armanuos. "ON THE EVALUATION OF THE GRADIENT TREE BOOSTING MODEL FOR GROUNDWATER LEVEL FORECASTING." Knowledge-Based Engineering and Sciences 1, no. 01 (December 31, 2020): 48–57. http://dx.doi.org/10.51526/kbes.2020.1.01.48-57.

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Though groundwater is a replenishable resource, it’s over exploitation has posed greater problem of its depletion. Hence, monitoring and forecasting of groundwater levels has become a primary task of governmental water boards/agencies for sustainable water management. The current study focused on evaluating the performance of Gradient Tree Boosting (GTB) model with that of conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) and Group Method of Data Handling (GMDH) models in forecasting groundwater levels of two coastal aquifers. Data of two groundwater level monitoring wells penetrating into unconfined aquifers located at Shirtadi and Rayee near to Mangalore city of Karnataka state, India was considered in the present study. Monthly groundwater level data of the years 2000 – 2013 were used for model simulation; wherein 70% of data was used for model training and the remaining 30% served as testing data. Comparative result evaluation shows that the proposed GTB approach for one month ahead groundwater level forecasting was giving much accurate results than the other models for the same period of time and same set of data. For Rayee monitoring well, the error statistic, RRMSE of GTB, GMDH and ANFIS models obtained during test phase were 0.473, 0.517 and 0.7522, respectively. The comparison is examined further with different performance metrics.
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23

Najafzadeh, Mohammad. "Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions." Ocean Engineering 99 (May 2015): 85–94. http://dx.doi.org/10.1016/j.oceaneng.2015.01.014.

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24

Graf, Renata, and Pouya Aghelpour. "Daily River Water Temperature Prediction: A Comparison between Neural Network and Stochastic Techniques." Atmosphere 12, no. 9 (September 7, 2021): 1154. http://dx.doi.org/10.3390/atmos12091154.

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The temperature of river water (TRW) is an important factor in river ecosystem predictions. This study aims to compare two different types of numerical model for predicting daily TRW in the Warta River basin in Poland. The implemented models were of the stochastic type—Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)—and the artificial intelligence (AI) type—Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function (RBF) and Group Method of Data Handling (GMDH). The ANFIS and RBF models had the most fitted outputs and the AR, ARMA and ARIMA patterns were the most accurate ones. The results showed that both of the model types can significantly present suitable predictions. The stochastic models have somewhat less error with respect to both the highest and lowest TRW deciles than the AIs and were found to be better for prediction studies, with the GMDH complex model in some cases reaching Root Mean Square Error (RMSE) = 0.619 °C and Nash-Sutcliff coefficient (NS) = 0.992, while the AR(2) simple linear model with just two inputs was partially able to achieve better results (RMSE = 0.606 °C and NS = 0.994). Due to these promising outcomes, it is suggested that this work be extended to other catchment areas to extend and generalize the results.
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Harandizadeh, Hooman, Danial Jahed Armaghani, and Edy Tonnizam Mohamad. "Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets." Neural Computing and Applications 32, no. 17 (February 27, 2020): 14047–67. http://dx.doi.org/10.1007/s00521-020-04803-z.

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26

Mozaffari, Ahmad, Alireza Fathi, and Saeed Behzadipour. "An evolvable self-organizing neuro-fuzzy multilayered classifier with group method data handling and grammar-based bio-inspired supervisors for fault diagnosis of hydraulic systems." International Journal of Intelligent Computing and Cybernetics 7, no. 1 (March 4, 2014): 38–78. http://dx.doi.org/10.1108/ijicc-06-2013-0034.

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Purpose – The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits. Design/methodology/approach – In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner. Findings – Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC. Originality/value – The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.
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27

Zaychenko, Yuriy P., and Igor O. Zayets. "Study of Efficiency of Fuzzy GMDH with Different Forms of Partial Descriptions and Adaptation Algorithms in Prognosis Problems." Journal of Automation and Information Sciences 40, no. 4 (2008): 62–74. http://dx.doi.org/10.1615/jautomatinfscien.v40.i4.50.

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Najafzadeh, Mohammad, and Hossein Bonakdari. "Application of a Neuro-Fuzzy GMDH Model for Predicting the Velocity at Limit of Deposition in Storm Sewers." Journal of Pipeline Systems Engineering and Practice 8, no. 1 (February 2017): 06016003. http://dx.doi.org/10.1061/(asce)ps.1949-1204.0000249.

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SHIMIZU, Eihan, and Toshiharu KOJIMA. "The Potential for Application of GMDH to Fuzzy Classification of Satellite Image Based on Comparison with Neural Network." Journal of the Japan society of photogrammetry and remote sensing 33, no. 2 (1994): 4–11. http://dx.doi.org/10.4287/jsprs.33.2_4.

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Najafzadeh, Mohammad. "Neuro-fuzzy GMDH based particle swarm optimization for prediction of scour depth at downstream of grade control structures." Engineering Science and Technology, an International Journal 18, no. 1 (March 2015): 42–51. http://dx.doi.org/10.1016/j.jestch.2014.09.002.

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Martinez-Leon, Juan-Antonio, Jose-Manuel Cano-Izquierdo, and Julio Ibarrola. "Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI." Computational Intelligence and Neuroscience 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/781207.

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This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate. This way, it is possible to build cheaper, quicker, and more portable BCI systems. The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest. Furthermore, a new three-step methodology has been developed which includes a feature discriminant character calculation stage; a score, order, and selection phase; and a final feature selection step. For the first stage, both statistics method and fuzzy criteria are used. The fuzzy criteria are based on the S-dFasArt classification algorithm which has shown excellent performance in previous papers undertaking the BCI multiclass motor imagery problem. The score, order, and selection stage is used to sort the features according to their discriminant nature. Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.
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32

Beheshti, A. A., and B. Ataie-Ashtiani. "Discussion of “Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions” by M. Najafzadeh." Ocean Engineering 123 (September 2016): 249–52. http://dx.doi.org/10.1016/j.oceaneng.2016.07.005.

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Aghelpour, Pouya, Babak Mohammadi, Seyed Mostafa Biazar, Ozgur Kisi, and Zohreh Sourmirinezhad. "A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods." ISPRS International Journal of Geo-Information 9, no. 12 (November 25, 2020): 701. http://dx.doi.org/10.3390/ijgi9120701.

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Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.
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Kumar, Manish, Abidhan Bardhan, Pijush Samui, Jong Wan Hu, and Mosbeh R. Kaloop. "Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study." Processes 9, no. 3 (March 8, 2021): 486. http://dx.doi.org/10.3390/pr9030486.

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Uncertainty and variability are inherent to pile design and consequently, there have been considerable researches in quantifying the reliability or probability of failure of structures. This paper aims at examining and comparing the applicability and adaptability of Minimax Probability Machine Regression (MPMR), Emotional Neural Network (ENN), Group Method of Data Handling (GMDH), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the reliability analysis of pile embedded in cohesionless soil and proposes an AI-based prediction method for bearing capacity of pile foundation. To ascertain the homogeneity and distribution of the datasets, Mann–Whitney U (M–W) and Anderson–Darling (AD) tests are carried out, respectively. The performance of the developed soft computing models is ascertained using various statistical parameters. A comparative study is implemented among reliability indices of the proposed models by employing First Order Second Moment Method (FOSM). The results of FOSM showed that the ANFIS approach outperformed other models for reliability analysis of bearing capacity of pile and ENN is the worst performing model. The value of R2 for all the developed models is close to 1. The best RMSE value is achieved for the training phase of the ANFIS model (0 in training and 2.13 in testing) and the poorest for the ENN (2.03 in training and 31.24 in testing) model. Based on the experimental results of reliability indices, the developed ANFIS model is found to be very close to that computed from the original data.
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Wong, Kok Wai, Tamás Gedeon, and Chun Che Fung. "Special Issue on Advances in Intelligent Data Processing." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 3 (March 20, 2007): 259–60. http://dx.doi.org/10.20965/jaciii.2007.p0259.

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Technological advancement using intelligent techniques has provided solutions to many applications in diverse engineering disciplines. In application areas such as web mining, image processing, medical, and robotics, just one intelligent data processing technique may be inadequate for handling a task, and a combination or hybrid of intelligent data processing techniques becomes necessary. The sharp increase in activities in the development of innovative intelligent data processing technologies also attracted the interest of many researchers in applying intelligent data processing techniques in other application domains. In this special issue, we presented 12 research papers focusing on different aspects of intelligent data processing and its applications. We start with a paper entitled "An Activity Monitor Design Based on Wavelet Analysis and Wireless Sensor Networks," which focuses on using wavelet analysis and wireless sensor networks for monitoring the human physical condition. The second paper, "An Approach in Designing Hierarchy of Fuzzy Behaviors for Mobile Robot Navigation," presents a hierarchical approach using fuzzy theory to assist in the task of mobile robot navigation. It also discusses the design of hierarchical behavior of mobile robots using sensors. The third paper, "Toward Natural Communication: Human-Robot Gestural Interaction Using Pointing," also works with robots focusing more on the interaction between users and robots in which the robot recognizes pointing by a human user through intelligent data processing. The fourth paper, "Embodied Conversational Agents for H5N1 Pandemic Crisis," examines the use of intelligent software bots as an interaction tool for crisis communication. linebreaknewpage The work is based on a novel Automated Knowledge Extraction Agent (AKEA). There are many interests of using intelligent data processing techniques for image processing and analysis, as shown in the next few papers. The fifth paper, "A Feature Vector Approach for Inter-Query Learning for Content-Based Image Retrieval," presents relevance feedback based technique for content based image retrieval. It extends the relevance feedback approach to capture the inter-query relationship between current and previous queries. The sixth paper, "Abstract Image Generation Based on Local Similarity Pattern," also falls in the area of image retrieval using local similarity patterns to generate abstract images from a given set of images. Along the same line of similarity measure for image retrieval, the seventh paper, "Cross-Resolution Image Similarity Modeling," works on cross resolution using probabilistic and fuzzy theory to formulate cross resolution image similarity modeling. The eighth paper, "Bayesian Spatial Autoregressive for Reducing Blurring Effect in Image," presents a Bayesian Spatial Autoregressive technique developed by Geweke and LeSage. The ninth paper, "Logistic GMDH-Type Neural Network and its Application to Identification of X-Ray Film Characteristic Curve," presents a class of neural networks for X-Ray Film processing and compares results with some conventional techniques. As digital entertainment and games grow increasingly popular, the tenth paper, "Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy," looks into the use of intelligent data processing for classifying of online game players. The eleventh paper, "Parallel Learning Model and Topological Measurement for Self-Organizing Maps," presents the concept of a SOM parallel learning model that appears both robust and efficient. The twelfth paper, "Optimal Size Fuzzy Models," delineates concepts on how to make fuzzy systems more efficient. As guest editors for this issue, we thank the authors for their hard work. We also thank the reviewers for their assistance in the review process. All full papers submitted to this special issue have been peer-reviewed by at least two international reviewers in the area.
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Onisawa, Takehisa. "Special Issue on Selected Papers in SCIS & ISIS 2004 – No.2." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (May 20, 2005): 225. http://dx.doi.org/10.20965/jaciii.2005.p0225.

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The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) held at Keio University in Yokohama, Japan, on September 21-24, 2004, attracted over 300 papers in fields such as mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks applications, evolutionary computation, system modeling, fuzzy measures, and robotics. The Program Committee requested reviewers in SCIS & ISIS 2004 to select papers for a special issue of the Journal of Advanced Computational Intelligence & Intelligent Informatics (JACIII), with 27 papers accepted for publication in a two-part SCIS & ISIS 2004 special – Vol.9, No.2, containing 13 and the second part containing 14. Paper 1 details tap-changer control using neural networks. Papers 2-5 deal with image processing and recognition – Paper 2 proposing a model of saliency-driven scene learning and recognition and applying its model to robotics, paper 3 discussing breast cancer recognition using evolutionary algorithms, paper 4 covering a revised GMDH-typed neural network model applied to medical image recognition, paper 5 presenting how to compensate for missing information in the acquisition of visual information applied to autonomous soccer robot control. Paper 6 details gene expressions networks for 4 fruit fly development stages. Paper 7 proposes an α-constrained particle swarm optimized for solving constrained optimization problem. Paper 8 develops a fuzzy-neuro multilayer perceptron using genetic algorithms for recognizing odor mixtures. Paper 9 discusses how to integrate symbols into neural networks for the fusion of computational and symbolic processing and its effectiveness demonstrated through simulations. Paper 10 proposes an electric dictionary using a set of nodes and links whose usefulness is verified in experiments. Paper 11 presents a multi-agent algorithm for a class scheduling problem, showing its feasibility through computer simulation. Paper 12 proposes inductive temporal formula specification in system verification, reducing memory and time in the task of system verification. Paper 13 applies an agent-based approach to modeling transport using inductive learning by travelers and an evolutionary approach. The last paper analyzes architectural floor plans using a proposed index classifying floor plans from the user's point of view. We thank reviewers for their time and effort in making these special issues available so quickly, and thank the JACIII editorial board, especially Editor-in-Chief Profs. Hirota and Fukuda and Managing Editor Kenta Uchino, for their invaluable aid and advice in putting these special issues together.
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Valdés, Lissette, Alfonso Ariza, Sira M. Allende, Alicia Triviño, and Gonzalo Joya. "Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions." Symmetry 13, no. 8 (August 20, 2021): 1534. http://dx.doi.org/10.3390/sym13081534.

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A communication network management system takes the measurements of its state variables at specific instants of time, considering them constant in the interval between two consecutive measurements. Nevertheless, this assumption is not true, since these variables evolve in real time. Therefore, uncertainty is inherent in the processing of the measurements during the intervals so that they cannot be efficiently managed using crisp variables. In this paper, we face this problem by modeling the communications network as a type-V fuzzy graph, where both the nodes and the links are described with precision, but the cost of each link is modeled as a triangular fuzzy number. Different fuzzy cost allocation functions and fuzzy optimization strategies are described and applied to the search for the shortest path between two nodes. An experimental study has been conducted using two representative networks: the backbone network of Nippon Telegraph and Telephone Corporation (NTT) and the National Science Foundation’s Network (NFSNET). In these networks, our fuzzy cost functions and strategies have been compared with the well-known crisp equivalents. The optimal search strategies are based on the proposed Fuzzy Dijkstra Algorithm (FDA), which is described deeply. The simulation results demonstrate that in all cases the fuzzy alternatives surpass or equal the crisp equivalents with statistically significant values. Specifically, the so-called Strategy 8 presents the best throughput, as it significantly exceeds the performance of all those evaluated, achieving a Global Mean Delivery Rate (GMDR) close to 1.
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38

Brailko, A. A., O. V. Gromov, and L. A. Druzhinin. "Digital technologies are the basis of digital economy of civil aviation airport refueling complexes." Civil Aviation High Technologies 23, no. 4 (September 4, 2020): 20–32. http://dx.doi.org/10.26467/2079-0619-2020-23-4-20-32.

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The article analyzes the main information automated control systems for refueling complexes, based on this analysis, the problems of the airport ground handling functioning are identified, the main of which are the inefficiency of managing stochastic processes that occur in failure situations, as well as the lack of automated control systems for the level of purity of aviation fuel from mechanical impurities and water. The way to upgrade Groundstar Inform GmbH - a single integrated airport management system by increasing the capabilities of the system by adding new components is proposed. A solution to the problem of multi-resource planning of aircraft refueling in high-intensity flight conditions, including failure situations, based on intelligent simulation and resource management is proposed. As well as from the point of view of optimizing the solution of business process objectives the development of planning algorithms using the mathematical apparatus of fuzzy modeling and control, fuzzy sets and fuzzy logic underlying the intelligent modeling of processes is proposed. The concept of an adaptive information management system of technological processes of a refueling complex for monitoring the purity of jet fuel, based on dynamic on-line monitoring of the existence of mechanical impurities and water is introduced. The article examines the elements of creating a "Smart Refueling Complex", in which intelligent business processes are combined into one whole due to the use of "smart" operational processes and technological equipment. The introduction of digital technologies, "industry 4.0" tools and trends in automation, digitalization and digitalization of the modern aviation fuel supply for civil aviation is becoming the basis of the digital economy of civil aviation refueling complexes.
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39

Sorochak, Oleg, and Svitlana Kvak. "The Model for Selection of Innovation and Investment Strategy of Machine-Building Enterprises: Practical Aspect." Marketing and Management of Innovations, no. 2 (2020): 68–84. http://dx.doi.org/10.21272/mmi.2020.2-05.

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The article summarizes the arguments within the scientific challenge in choosing the innovation and investment strategy of machine-building enterprises. The main objective of the research is to develop a model for choosing the optimal strategy for innovation and investment development of machine-building enterprises, taking into account their level of investment attractiveness in the market and the level of innovative capacity as well. Systematization of theoretical and methodological material on the model of choosing the innovation and investment strategy of machine-building enterprises has given grounds to conclude that further research is required regarding the modeling issues based on the indicators of a sophisticated analysis of the investment attractiveness of the enterprise and determining its level of innovation potential in order to increase the efficiency of the domestic machine-building enterprises. The relevance of solving this problem is that it is the right choice of a particular type of innovation and investment strategy will help machine-building enterprises to improve the efficiency of their operation and to establish competitive positions in the market. The methodical tools of the research on the model of choosing the innovation and investment strategy are the matrix and convolution methods based on the fuzzy-set theory. The objects of the research JV «Spheros-Elektron» LLC, LEONI Wiring Systems UA GmbH and PJSC «Drohobych Truck Crane Plant» were chosen because they reveal peculiarities of the machine-building enterprises' operation in L'viv region. The paper presented the findings of the choice of appropriate innovation and investment strategies, which showed that PJSC «Drohobych Truck Crane Plant» has entered the zone of application of the strategy of innovation changes and is characterized by both low level of investment attractiveness and low level of the innovative potential. JV «Spheros-Elektron» LLC and LEONI Wiring Systems UA GmbH – both have entered the zone of application of the strategy of differentiation, characterized by an average level of investment attractiveness and a satisfactory level of innovation potential. However, according to the first parameter – the level of investment attractiveness of the company on the market, LEONI Wiring Systems UA GmbH has also approached and is close enough to the application of the opportunistic strategy. The research empirically confirms and theoretically proves that in order to remain on the market of PJSC «Drohobych Truck Crane Plant», it is necessary to modernize the products radically. Furthermore, the main tasks of implementing the differentiation strategy for «Spheros-Elektron» and «LEONI» should be: to invest in the activities of research institutions; to conduct consumer preferences analysis; the analysis of its production capacities; installation of new production lines according to the achievements of the technical progress; production output, which is in demand by the population; holding a capable advertising company. The results of the research can be useful for industrial enterprises in general and machine-building enterprises, in particular. Keywords: fuzzy-set theory, innovation and investment strategies, machine-building enterprise, the model for the selection of the strategy, the strategy of innovation development.
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40

Tien Bui, Dieu, and Nhat-Duc Hoang. "A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods." Geoscientific Model Development 10, no. 9 (September 14, 2017): 3391–409. http://dx.doi.org/10.5194/gmd-10-3391-2017.

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Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.
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41

Sandberg, Berit. "The Artist as Innovation Muse: Findings from a Residence Program in the Fuzzy Front End." Administrative Sciences 10, no. 4 (November 5, 2020): 88. http://dx.doi.org/10.3390/admsci10040088.

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In a highly competitive business environment, integrating artists into corporate research and development (R&D) seems to be a promising way to foster inventiveness and idea generation. Given the importance of individual level innovation for product development, this study explores the benefits that employees experience from the artist-in-residence-program at Robert Bosch GmbH, Germany. Qualitative content analysis of interviews with scientists and engineers was performed in order to explore the impact of their encounters with artists in the theoretical framework of the triadic concept and transmission model of inspiration. The findings corroborate the notion that inspiration is a suitable theoretical underpinning for individual benefits of art–science collaborations in the front end of innovation. Scientists and engineers are inspired by the artists’ otherness and transcend their usual modes of perception in favor of enhanced focal, peripheral and bifocal vision. Whereas shifts in perspective are reflected in individual thinking patterns, researchers are hardly motivated to change their work-related behavior. The exchange with artists does not have a concrete impact on technological innovation, because researchers neither integrate impulses into their experiential world nor link them to fields of activity. In the case under scrutiny, artistic impulses do not contribute to idea generation in the sense of front-end activities. The study contributes to research on artists in businesses by illuminating the R&D environment as a hitherto neglected field of activity. While substantiating previous research on artist-in-science-residencies, the results suggest that the potential of such interdisciplinary endeavors is limited.
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42

Joshi, Ranee, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot. "<i>dh2loop</i> 1.0: an open-source Python library for automated processing and classification of geological logs." Geoscientific Model Development 14, no. 11 (November 4, 2021): 6711–40. http://dx.doi.org/10.5194/gmd-14-6711-2021.

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Abstract. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as they are compiled and recorded in an unstructured textual form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. They are subjective and plagued by uncertainty as they are likely to have been conducted by tens to hundreds of geologists, all of whom would have their own personal biases. dh2loop (https://github.com/Loop3D/dh2loop, last access: 30 September 2021​​​​​​​) is an open-source Python library for extracting and standardizing geologic drill hole data and exporting them into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia (GSWA) Mineral Exploration Reports (WAMEX) database in the Yalgoo–Singleton greenstone belt (YSGB) region. The contribution also addresses the subjective nature and variability of the nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. For this study case, 86 % of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted in a matching rate of 16 % (7870 successfully matched records out of 47 823 records). The standardized lithological data are then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing Python libraries (lasio, welly, striplog).
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43

Shen, Xiaoli, Ramakrishna Ramisetty, Claudia Mohr, Wei Huang, Thomas Leisner, and Harald Saathoff. "Laser ablation aerosol particle time-of-flight mass spectrometer (LAAPTOF): performance, reference spectra and classification of atmospheric samples." Atmospheric Measurement Techniques 11, no. 4 (April 24, 2018): 2325–43. http://dx.doi.org/10.5194/amt-11-2325-2018.

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Abstract. The laser ablation aerosol particle time-of-flight mass spectrometer (LAAPTOF, AeroMegt GmbH) is able to identify the chemical composition and mixing state of individual aerosol particles, and thus is a tool for elucidating their impacts on human health, visibility, ecosystem, and climate. The overall detection efficiency (ODE) of the instrument we use was determined to range from ∼ (0.01 ± 0.01) to ∼ (4.23 ± 2.36) % for polystyrene latex (PSL) in the size range of 200 to 2000 nm, ∼ (0.44 ± 0.19) to ∼ (6.57 ± 2.38) % for ammonium nitrate (NH4NO3), and ∼ (0.14 ± 0.02) to ∼ (1.46 ± 0.08) % for sodium chloride (NaCl) particles in the size range of 300 to 1000 nm. Reference mass spectra of 32 different particle types relevant for atmospheric aerosol (e.g. pure compounds NH4NO3, K2SO4, NaCl, oxalic acid, pinic acid, and pinonic acid; internal mixtures of e.g. salts, secondary organic aerosol, and metallic core–organic shell particles; more complex particles such as soot and dust particles) were determined. Our results show that internally mixed aerosol particles can result in spectra with new clusters of ions, rather than simply a combination of the spectra from the single components. An exemplary 1-day ambient data set was analysed by both classical fuzzy clustering and a reference-spectra-based classification method. Resulting identified particle types were generally well correlated. We show how a combination of both methods can greatly improve the interpretation of single-particle data in field measurements.
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44

Meyerhoff, K. "Fuzzy Technologien - Prinzipien, Werkzeuge, Potentiale. VonH.-J. Zimmermann(Hrsg.). VDI Verlag GmbH, Düsseldorf 1993. 251 S., zahlr. Abb. u. Tab., Broschur. DIN A 5, DM 78.-." Chemie Ingenieur Technik 66, no. 11 (November 1994): 1534. http://dx.doi.org/10.1002/cite.330661126.

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45

Malekan, M., A. Khosravi, H. R. Goshayeshi, M. E. H. Assad, and J. J. Garcia Pabon. "Thermal Resistance Modeling of Oscillating Heat Pipes for Nanofluids by Artificial Intelligence Approach." Journal of Heat Transfer 141, no. 7 (May 14, 2019). http://dx.doi.org/10.1115/1.4043569.

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In this study, thermal resistance of a closed-loop oscillating heat pipe (OHP) is investigated using experimental tests and artificial intelligence methods. For this target, γFe2O3 and Fe3O4 nanoparticles are mixed with the base fluid. Also, intelligent models are developed to predict the thermal resistance of the OHP. These models are developed based on the heat input into evaporator section, the thermal conductivity of working fluids, and the ratio of the inner diameter to length of OHP. The intelligent methods are multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) type neural network. Thermal resistance of the heat pipe (as a measure of thermal performance) is considered as the target. The results showed that using the nanofluids as working fluid in the OHP decreased the thermal resistance, where this decrease for Fe3O4/water nanofluid was more than that of γFe2O3/water. The intelligent models also predicted successfully the thermal resistance of OHP with a correlation coefficient close to 1. The root-mean-square error (RMSE) for MLFFNN, ANFIS, and GMDH models was obtained as 0.0508, 0.0556, and 0.0569 (°C/W) (for the test data), respectively.
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46

Kher, Anupam, and Rajendra Yerpude. "Application and Comparative Assessment of Data Mining and Time Series Forecasting Models to Indian Coal Mining Production and Employment Parameters." International Journal of Next-Generation Computing, November 26, 2021. http://dx.doi.org/10.47164/ijngc.v12i5.472.

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Forecasting is designed to help decision making and planning before the actual event occurs. The main purposeof the application of time series forecasting models to Indian mining data is to get insight into the wide-rangingprinciples and methodologies for forecasting various parameters as well as current trends and future perspectives.This paper highlights the application of some major methods of time series forecasting such as the AutoregressiveIntegrated Moving Average (ARIMA) method, Regression method, Fuzzy Time Series method, Group Method ofData Handling (GMDH Model), and Neural Networks. Based on a series of comparative analyses depending uponthe capabilities and limitations of each model, the perspective of the multi-model based forecasting approach ispresented.
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47

Najafzadeh, Mohammad, and Ali Tafarojnoruz. "Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers." Environmental Earth Sciences 75, no. 2 (January 2016). http://dx.doi.org/10.1007/s12665-015-4877-6.

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48

Harandizadeh, Hooman, Danial Jahed Armaghani, Mahdi Hasanipanah, and Soheil Jahandari. "A novel TS Fuzzy-GMDH model optimized by PSO to determine the deformation values of rock material." Neural Computing and Applications, April 28, 2022. http://dx.doi.org/10.1007/s00521-022-07214-4.

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