Journal articles on the topic 'Feed-forward ANNs'
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1
Golpour, Iman, Ana Cristina Ferrão, Fernando Gonçalves, Paula M. R. Correia, Ana M. Blanco-Marigorta, and Raquel P. F. Guiné. "Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)." Foods 10, no. 9 (September 20, 2021): 2228. http://dx.doi.org/10.3390/foods10092228.
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This research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DPPH and ABTS antioxidant activities were considered as ANN outputs. The results demonstrate that the best cascade- and feed-forward backpropagation topologies of ANNs for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures, with the training algorithms of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, tansig-tansig-tansig and purelin-tansig-tansig, respectively. The best R2 values for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE = 0.0047), 0.9651 (MSE = 0.0035) and 0.9756 (MSE = 0.00286), respectively. According to the comparison of ANNs, the results showed that the cascade-forward backpropagation network showed better performance than the feed-forward backpropagation network for predicting the TPC, and the FFBP network, in predicting the DPPH and ABTS antioxidant activity factors, had more precision than the cascade-forward backpropagation network. The ANN technique is a potential method for estimating targeted total phenolic compounds and the antioxidant activity of strawberries.
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O'Reilly, G., C. C. Bezuidenhout, and J. J. Bezuidenhout. "Artificial neural networks: applications in the drinking water sector." Water Supply 18, no. 6 (January 31, 2018): 1869–87. http://dx.doi.org/10.2166/ws.2018.016.
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Abstract Artificial neural networks (ANNs) could be used in effective drinking water quality management. This review provides an overview about the history of ANNs and their applications and shortcomings in the drinking water sector. From the papers reviewed, it was found that ANNs might be useful modelling tools due to their successful application in areas such as pipes/infrastructure, membrane filtration, coagulation dosage, disinfection residuals, water quality, etc. The most popular ANNs applied were feed-forward networks, especially Multi-layer Perceptrons (MLPs). It was also noted that over the past decade (2006–2016), ANNs have been increasingly applied in the drinking water sector. This, however, is not the case for South Africa where the application of ANNs in distribution systems is little to non-existent. Future research should be directed towards the application of ANNs in South African distribution systems and to develop these models into decision-making tools that water purification facilities could implement.
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Daud, Suleman, Khan Shahzada, M. Tufail, and M. Fahad. "Stream Flow Modeling of River Swat Using Regression and Artificial Neural Networks (ANNs) Techniques." Advanced Materials Research 255-260 (May 2011): 679–83. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.679.
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This paper presents the utility of Artificial Neural Networks and Regression analysis for the stream flow modeling in Swat River at five discharge gauge station. As an appropriate intelligent model is identified, Artificial Neural Networks (ANNs) is evaluated by comparing it to regression analysis and the available field data. ANNs namely feed forward back propagation neural network (FFBPNN) and regression analysis are introduced and implemented. The research study successfully compared the performance of the ANN and regression models that validated the utility of the two modeling techniques as effective applications to stream flow forecasting.
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Novickis, Rihards, Daniels Jānis Justs, Kaspars Ozols, and Modris Greitāns. "An Approach of Feed-Forward Neural Network Throughput-Optimized Implementation in FPGA." Electronics 9, no. 12 (December 18, 2020): 2193. http://dx.doi.org/10.3390/electronics9122193.
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Artificial Neural Networks (ANNs) have become an accepted approach for a wide range of challenges. Meanwhile, the advancement of chip manufacturing processes is approaching saturation which calls for new computing solutions. This work presents a novel approach of an FPGA-based accelerator development for fully connected feed-forward neural networks (FFNNs). A specialized tool was developed to facilitate different implementations, which splits FFNN into elementary layers, allocates computational resources and generates high-level C++ description for high-level synthesis (HLS) tools. Various topologies are implemented and benchmarked, and a comparison with related work is provided. The proposed methodology is applied for the implementation of high-throughput virtual sensor.
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Al Khatib, Mohamed, and Samer Al Martini. "A Study on the Application of Artificial Neural Networks on Green Self Consolidating Concrete (SCC) under Hot Weather." Key Engineering Materials 677 (January 2016): 254–59. http://dx.doi.org/10.4028/www.scientific.net/kem.677.254.
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Self-consolidating concrete (SCC) has recently drawn attention to the construction industry in hot weather countries, due to its high fresh and mechanical properties. The slump flow is routinely used for quality control of SCC. Experiments were conducted by the current authors to investigate the effects of hot weather conditions on the slump flow of SCC. Self-consolidating concrete mixtures were prepared with different dosages of fly ash and superplasticizer and under different ambient temperatures. The results showed that the slump flow of SCC is sensitive to changes in ambient temperature, fly ash dosage, and superplasticizer dosage. In this paper, several artificial neural networks (ANNs) were employed to predict the slump flow of self-consolidating concrete under hot weather. Some of the data used to construct the ANNs models in this paper were collected from the experimental study conducted by the current authors, and other data were gathered from literature. Various parameters including ambient temperature and mixing time were used as inputs during the construction of ANN models. The developed ANN models employed two neural networks: the Feed-Forward Back Propagation (FFBP) and the Cascade Forward Back Propagation (CFBP). Both FFBP and CFBP showed good predictability to the slump flow of SCC mixtures. However, the FFBP network showed a slight better performance than CFBP, where it better predicted the slump flow of SCC than the CFBP network under hot weather. The results in this paper indicate that the ANNs can be employed to help the concrete industry in hot weather to predict the quality of fresh self-consolidating concrete mixes without the need to go through long trial and error testing program.Keywords: Self-consolidating concrete; Neural networks; Hot weather, Feed-forward back-propagation, Cascade-forward back propagation.
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Sabir, Zulqurnain, Thongchai Botmart, Muhammad Asif Zahoor Raja, Wajaree Weera, and Fevzi Erdoğan. "A stochastic numerical approach for a class of singular singularly perturbed system." PLOS ONE 17, no. 11 (November 28, 2022): e0277291. http://dx.doi.org/10.1371/journal.pone.0277291.
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In the present study, a neuro-evolutionary scheme is presented for solving a class of singular singularly perturbed boundary value problems (SSP-BVPs) by manipulating the strength of feed-forward artificial neural networks (ANNs), global search particle swarm optimization (PSO) and local search interior-point algorithm (IPA), i.e., ANNs-PSO-IPA. An error-based fitness function is designed using the differential form of the SSP-BVPs and its boundary conditions. The optimization of this fitness function is performed by using the computing capabilities of ANNs-PSO-IPA. Four cases of two SSP systems are tested to confirm the performance of the suggested ANNs-PSO-IPA. The correctness of the scheme is observed by using the comparison of the proposed and the exact solutions. The performance indices through different statistical operators are also provided to solve the SSP-BVPs using the proposed ANNs-PSO-IPA. Moreover, the reliability of the scheme is observed by taking hundred independent executions and different statistical performances have been provided for solving the SSP-BVPs to check the convergence, robustness and accuracy.
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Mahmoudi, Amir Hossein, Mitra Ghanbari-Matloob, and Soroush Heydarian. "A Neural Networks Approach to Measure Residual Stresses Using Spherical Indentation." Materials Science Forum 768-769 (September 2013): 114–19. http://dx.doi.org/10.4028/www.scientific.net/msf.768-769.114.
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In the present study an Artificial Neural Network (ANN) approach is proposed for residual stresses estimation in engineering components using indentation technique. First of all, load-penetration curves of indentation tests for tensile and compressive residual stresses are studied using Finite Element Method (FEM) for materials with different yield stresses and work-hardening exponents. Then, experimental tests are carried out on samples made of 316L steel without residual stresses. In the next step, multi-layer feed forward ANNs are created and trained based on 80% of obtained numerical data using Back-Error Propagation (BEP) algorithm. Then the trained ANNs are tested against the remaining data. The obtained results show that the predicted residual stresses are in good agreement with the actual data.
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Kaveh, M., and R. A. Chayjan. "Mathematical and neural network modelling of terebinth fruit under fluidized bed drying." Research in Agricultural Engineering 61, No. 2 (June 2, 2016): 55–65. http://dx.doi.org/10.17221/56/2013-rae.
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The paper presents an application which uses Feed Forward Neural Networks (FFNNs) to model the non-linear behaviour of the terebinth fruit drying. Mathematical models and Artificial Neural Networks (ANNs) were used for prediction of effective moisture diffusivity, specific energy consumption, shrinkage, drying rate and moisture ratio in terebinth fruit. Feed Forward Neural Network (FFBP) and Cascade Forward Neural Network (CFNN) as well as training algorithms of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used. Air temperature and velocity limits were 40–80°C and 0.81–4.35 m/s, respectively. The best outcome for the use of ANN for the effective moisture diffusivity appertained to CFNN network with BR training algorithm, topology of 2-3-1 and threshold function of TANSIG. Similarly, the best outcome for the use of ANN for drying rate and moisture ratio also appertained to CFNN network with LM training algorithm, topology of 3-2-4-2 and threshold function of TANSIG.
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Abujayyab, S. K. M., M. A. S. Ahamad, A. S. Yahya, and A. M. H. Y. Saad. "A NEW FRAMEWORK FOR GEOSPATIAL SITE SELECTION USING ARTIFICIAL NEURAL NETWORKS AS DECISION RULES: A CASE STUDY ON LANDFILL SITES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (October 19, 2015): 131–38. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-131-2015.
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This paper briefly introduced the theory and framework of geospatial site selection (GSS) and discussed the application and framework of artificial neural networks (ANNs). The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015. As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decision-making problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS.
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Gallo, Mariano, and Giuseppina De Luca. "Spatial Extension of Road Traffic Sensor Data with Artificial Neural Networks." Sensors 18, no. 8 (August 12, 2018): 2640. http://dx.doi.org/10.3390/s18082640.
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This paper proposes a method for estimating traffic flows on some links of a road network knowing the data on other links that are monitored with sensors. In this way, it is possible to obtain more information on traffic conditions without increasing the number of monitored links. The proposed method is based on artificial neural networks (ANNs), wherein the input data are the traffic flows on some monitored road links and the output data are the traffic flows on some unmonitored links. We have implemented and tested several single-layer feed-forward ANNs that differ in the number of neurons and the method of generating datasets for training. The proposed ANNs were trained with a supervised learning approach where input and output example datasets were generated through traffic simulation techniques. The proposed method was tested on a real-scale network and gave very good results if the travel demand patterns were known and used for generating example datasets, and promising results if the demand patterns were not considered in the procedure. Numerical results have underlined that the ANNs with few neurons were more effective than the ones with many neurons in this specific problem.
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Hettiarachchi, P., M. J. Hall, and A. W. Minns. "The extrapolation of artificial neural networks for the modelling of rainfall—runoff relationships." Journal of Hydroinformatics 7, no. 4 (October 1, 2005): 291–96. http://dx.doi.org/10.2166/hydro.2005.0025.
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The last decade has seen increasing interest in the application of Artificial Neural Networks (ANNs) for the modelling of the relationship between rainfall and streamflow. Since multi-layer, feed-forward ANNs have the property of being universal approximators, they are able to capture the essence of most input–output relationships, provided that an underlying deterministic relationship exists. Unfortunately, owing to the standardisation of inputs and outputs that is required to run ANNs, a problem arises in extrapolation: if the training data set does not contain the maximum possible output value, an unmodified network will be unable to synthesise this peak value. The occurrence of high magnitude, low frequency events within short periods of record is largely fortuitous. Therefore, the confidence in the neural network model can be greatly enhanced if some methodology can be found for incorporating domain knowledge about such events into the calibration and verification procedure in addition to the available measured data sets. One possible form of additional domain knowledge is the Estimated Maximum Flood (EMF), a notional event with a small but non-negligible probability of exceedence. This study investigates the suitability of including an EMF estimate in the training set of a rainfall–runoff ANN in order to improve the extrapolation characteristics of the network. A study has been carried out in which EMFs have been included, along with recorded flood events, in the training of ANN models for six catchments in the south west of England. The results demonstrate that, with prior transformation of the runoff data to logarithms of flows, the inclusion of domain knowledge in the form of such extreme synthetic events improves the generalisation capabilities of the ANN model and does not disrupt the training process. Where guidelines are available for EMF estimation, the application of this approach is recommended as an alternative means of overcoming the inherent extrapolation problems of multi-layer, feed-forward ANNs.
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Shahbazian, Alireza, Hamidreza Rabiefar, and Babak Aminnejad. "Shear Strength Determination in RC Beams Using ANN Trained with Tabu Search Training Algorithm." Advances in Civil Engineering 2021 (November 24, 2021): 1–14. http://dx.doi.org/10.1155/2021/1639214.
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The shear failure of reinforced concrete (RC) beams is a critical issue and has attracted the attention of researchers. The specific challenges of shear failure are the numerous factors affecting shear strength, the nonlinear behavior, and the nonlinear relationship between affecting parameters and the concrete properties. This study tackles this challenge by employing Artificial Neural Network (ANN) models. Since, according to No Free Lunch theorem, the performance of optimization algorithms is problem-dependent, this paper aims to assess the feasibility of modeling the shear strength of RC beams using ANNs trained with the Tabu Search Training (TST) algorithm. To this end, 248 experimental results were collected from the literature, and a feed-forward ANN model was employed to predict the shear strength. To assess its feasibility, the ANNs were also modeled using the Particle Swarm Optimization, and Imperialist Competitive Algorithms. As a traditional technique, the multiple regression model was also employed. The shear design equations of ACI-318-2019 were also investigated and compared with Tabu Search Trained ANN model. The analysis of results suggests the superiority of Tabu Search Trained ANNs in comparison to other suggested models in literature and the ACI-318-2019 design code.
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Aalimahmoody, Nasrin, Chiara Bedon, Nasim Hasanzadeh-Inanlou, Amir Hasanzade-Inallu, and Mehdi Nikoo. "BAT Algorithm-Based ANN to Predict the Compressive Strength of Concrete—A Comparative Study." Infrastructures 6, no. 6 (May 26, 2021): 80. http://dx.doi.org/10.3390/infrastructures6060080.
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The number of effective factors and their nonlinear behaviour—mainly the nonlinear effect of the factors on concrete properties—has led researchers to employ complex models such as artificial neural networks (ANNs). The compressive strength is certainly a prominent characteristic for design and analysis of concrete structures. In this paper, 1030 concrete samples from literature are considered to model accurately and efficiently the compressive strength. To this aim, a Feed-Forward (FF) neural network is employed to model the compressive strength based on eight different factors. More in detail, the parameters of the ANN are learned using the bat algorithm (BAT). The resulting optimized model is thus validated by comparative analyses towards ANNs optimized with a genetic algorithm (GA) and Teaching-Learning-Based-Optimization (TLBO), as well as a multi-linear regression model, and four compressive strength models proposed in literature. The results indicate that the BAT-optimized ANN is more accurate in estimating the compressive strength of concrete.
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Berus, Lucijano, Simon Klancnik, Miran Brezocnik, and Mirko Ficko. "Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks." Sensors 19, no. 1 (December 20, 2018): 16. http://dx.doi.org/10.3390/s19010016.
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In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.
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Demiralay, Raziye, Ibrahim Akdenizli, and Halime Boztoprak. "Estimating of student success with artificial neural networks." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 7 (July 23, 2017): 21–27. http://dx.doi.org/10.18844/prosoc.v3i7.1980.
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Purpose of the study is classifying students’ success of Information and Communication Technology-ICT courses by using Artificial-Neural-Networks-ANNs models. To classify success of 161 students, a three-layer-feed-forward-ANNs model is used. 3 parameters which contain demographic data and 27 parameters which contain ICT-Usage data captured by a questionnaire chosen as input layer parameters. Hidden nodes are determined experimentally. Logic-0 and Logic-1 are the output level values which define success of the students in ICT courses. The Back-Propagation algorithm is used for training of ANN. The mean squares of the errors are used as a performance (error) function with its goal set to zero. In conclusion, the application done with success ratio of 96%. If same variables are used, realistic estimations will be reached. It is recommended that a research with same parameters would be better results with higher participation. Keywords: Artificial Neural network, backpropagation, student success, classification, ICT.
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Suchacz, Bogdan, and Marek Wesolowski. "Herbal drug raw materials differentiation by neural networks using non-metals content." Open Chemistry 8, no. 6 (December 1, 2010): 1298–304. http://dx.doi.org/10.2478/s11532-010-0105-0.
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AbstractThree-layer artificial neural networks (ANN) capable of recognizing the type of raw material (herbs, leaves, flowers, fruits, roots or barks) using the non-metals (N, P, S, Cl, I, B) contents as inputs were designed. Two different types of feed-forward ANNs — multilayer perceptron (MLP) and radial basis function (RBF), best suited for solving classification problems, were used. Phosphorus, nitrogen, sulfur and boron were significant in recognition; chlorine and iodine did not contribute much to differentiation. A high recognition rate was observed for barks, fruits and herbs, while discrimination of herbs from leaves was less effective. MLP was more effective than RBF.
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Kechagias, John, Aristidis Tsiolikas, Panagiotis Asteris, and Nikolaos Vaxevanidis. "Optimizing ANN performance using DOE: application on turning of a titanium alloy." MATEC Web of Conferences 178 (2018): 01017. http://dx.doi.org/10.1051/matecconf/201817801017.
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A methodology is presented to optimize the performance of an Artificial Neural Network (ANN) using Design of Experiments (DOE). 8 different feed forward back propagation (FFBP) ANNs were developed and tested according to the L8 full factorial orthogonal array. The 3 parameters tested were: Number of Hidden Neurons, Learning rate, and Momentum; each one having two levels. By utilizing the analysis of means (ANOM) and the analysis of variances (ANOVA), the optimum levels of ANN parameters were determined. The developed ANN was applied for predicting cutting forces and average surface roughness in turning Ti-6Al-4V alloy.
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Srisaeng, Panarat, Glenn S. Baxter, and Graham Wild. "FORECASTING DEMAND FOR LOW COST CARRIERS IN AUSTRALIA USING AN ARTIFICIAL NEURAL NETWORK APPROACH." Aviation 19, no. 2 (June 24, 2015): 90–103. http://dx.doi.org/10.3846/16487788.2015.1054157.
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This study focuses on predicting Australia‘s low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia‘s real GDP, real GDP per capita, air fares, Australia‘s population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively.
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Nourani, Vahid, Elnaz Sharghi, and Mohammad Hossein Aminfar. "Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran." Artificial Intelligence Research 1, no. 2 (August 30, 2012): 22. http://dx.doi.org/10.5430/air.v1n2p22.
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Piezometric heads in the core of Sattarkhan earthfill dam in Iran have been analyzed in this paper via Artificial Neural Network (ANN). Single and integrated ANN models were trained and verified using each piezometer’s data, and also the water levels on the up and downstream of the dam. Therefore, in the single ANN modeling a single ANN was developed for each piezometer, whereas in the integrated ANN modeling only a unique ANN was trained for all piezometers at different cross sections of the dam. Three-layered Perceptron ANN trained with Back Propagation Levenberg-Marquardt scheme was employed in the single modeling; while, two different ANN algorithms, the feed-forward back-propagation (FFBP) and the radial basis function (RBF) were employed to develop integrated ANNs. The number of hidden neurons were determined 5 and 7 for single ANNs, whereas 6 hidden neurons for the integrated FFBP ANN, and the spread value of 0.5 for the integrated RBF. The results show good agreement between computed and observed water heads at different monitoring piezometers with validation determination coefficients higher than 0.7984 in the single and 0.87 and 0.67 in the FFBP and RBF integrated modeling, respectively. Thereafter, the results of the ANNs were satisfactorily compared with the results of a physically based model (Finite Element Model, FEM).
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González-Pérez, Carlos Alberto, and Jaime De-la-Colina. "Determination of Mass Properties in Floor Slabs from the Dynamic Response Using Artificial Neural Networks." Civil Engineering Journal 8, no. 8 (August 1, 2022): 1549–64. http://dx.doi.org/10.28991/cej-2022-08-08-01.
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Most of the research on accidental eccentricity is directed at both the evaluation of accidental eccentricity design code recommendations and the study of building torsional response. In contrast, this paper addresses how the mass properties of each of the levels of a building could be determined from the dynamic response of a building. Using the dynamic response of buildings, this paper presents the application of multilayer feed forward artificial neural networks (ANNs) to determine the magnitude, the radial distance, and the polar moment of inertia of the mass for each level of reinforced concrete (RC) buildings. Analytical models were developed for three regular buildings. Live-load magnitude and mass position are considered as random variables. Seven load cases were generated for the 1, 2 and 4-story models using two excitations. As for the input parameters of the ANNs, three different choices of input data to the network were used. The developed ANN models are able to predict with adequate accuracy the radial position, magnitude, and polar moment of inertia of masses of each level. The implementation of this method based on ANNs would allow the monitoring, either permanently or temporarily, of changes in mass properties at each building floor slab. Doi: 10.28991/CEJ-2022-08-08-01 Full Text: PDF
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Raja, Muhammad Asif Zahoor, Kiran Asma, and Muhammad Saeed Aslam. "Bio-inspired computational heuristics to study models of HIV infection of CD4+ T-cell." International Journal of Biomathematics 11, no. 02 (February 2018): 1850019. http://dx.doi.org/10.1142/s1793524518500195.
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In this work, biologically-inspired computing framework is developed for HIV infection of CD4[Formula: see text] T-cell model using feed-forward artificial neural networks (ANNs), genetic algorithms (GAs), sequential quadratic programming (SQP) and hybrid approach based on GA-SQP. The mathematical model for HIV infection of CD4[Formula: see text] T-cells is represented with the help of initial value problems (IVPs) based on the system of ordinary differential equations (ODEs). The ANN model for the system is constructed by exploiting its strength of universal approximation. An objective function is developed for the system through unsupervised error using ANNs in the mean square sense. Training with weights of ANNs is carried out with GAs for effective global search supported with SQP for efficient local search. The proposed scheme is evaluated on a number of scenarios for the HIV infection model by taking the different levels for infected cells, natural substitution rates of uninfected cells, and virus particles. Comparisons of the approximate solutions are made with results of Adams numerical solver to establish the correctness of the proposed scheme. Accuracy and convergence of the approach are validated through the results of statistical analysis based on the sufficient large number of independent runs.
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Brkić, Srđan, Predrag Ivaniš, and Bane Vasić. "On guaranteed correction of error patterns with artificial neural networks." Telfor Journal 14, no. 2 (2022): 51–55. http://dx.doi.org/10.5937/telfor2202051b.
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In this paper, we analyze applicability of single-and two-hidden-layer feed-forward artificial neural networks, SLFNs and TLFNs, respectively, in decoding linear block codes. Based on the provable capability of SLFNs and TLFNs to approximate discrete functions, we discuss sizes of the network capable to perform maximum likelihood decoding. Furthermore, we propose a decoding scheme, which use artificial neural networks (ANNs) to lower the error-floors of low-density parity-check (LDPC) codes. By learning a small number of error patterns, uncorrectable with typical decoders of LDPC codes, ANN can lower the error-floor by an order of magnitude, with only marginal average complexity incense.
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Song, Yang, Dawei Han, and Miguel A. Rico-Ramirez. "High temporal resolution rainfall rate estimation from rain gauge measurements." Journal of Hydroinformatics 19, no. 6 (August 24, 2017): 930–41. http://dx.doi.org/10.2166/hydro.2017.054.
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Abstract Rainfall rates derived from tipping bucket rain gauges generally ignore the detailed variation at a finer temporal scale that particularly occurs in light rainfall events. This study extends the exploration of using artificial neural networks (ANNs), in comparison with the conventional linear interpolation method (LIM) and the cubic spline algorithm (CSA) for rainfall rate estimation at fine temporal resolution using rain gauge data based on a case study at Chilbolton and Sparsholt observatories, UK. A supervised feed-forward neural network integrated with the backpropagation algorithm is used to identify the complex nonlinear relationships between input and target variables. The results indicate that the ANN considerably outperforms the CSA and LIM with higher Nash–Sutcliffe efficiency, lower root mean square error and lower rainfall amount differences when compared to the disdrometer observations when the model is trained within a broad span of input values. Consistent stability in accurately estimating rainfall rate in different sites shows the intrinsic advantage of ANNs in learning and self-adaptive abilities in modelling complex nonlinear relationships between the inputs and target variables.
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Malik, Samander Ali, Assad Farooq, Thomas Gereke, and Chokri Cherif. "Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression." Autex Research Journal 16, no. 2 (June 1, 2016): 43–50. http://dx.doi.org/10.1515/aut-2015-0018.
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Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.
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Raja, Muhammad Asif Zahoor, Mohmmad Abdul Rehman Khan, Tariq Mahmood, Umair Farooq, and Naveed Ishtiaq Chaudhary. "Design of bio-inspired computing technique for nanofluidics based on nonlinear Jeffery–Hamel flow equations." Canadian Journal of Physics 94, no. 5 (May 2016): 474–89. http://dx.doi.org/10.1139/cjp-2015-0440.
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In this study, stochastic numerical treatment is presented for boundary value problems (BVPs) arising in nanofluidics for nonlinear Jeffery–Hamel flow (NJ-HF) equations using feed-forward artificial neural networks (ANNs) optimized with bio-inspired computing based on genetic algorithms (GAs) integrated with the active-set method (ASM). NJ-HF equations associated with both convergent and divergent channels, involving nanoparticles, are derived from the transformation of Navier–Stokes partial differential equations to nonlinear BVPs of third-order ordinary differential equations. The mathematical model of the transformed BVPs is developed with the help of ANNs in an unsupervised manner and the design parameters of these networks are trained with GAs, ASM, and GA–ASM. The design scheme is evaluated for NJ-HF by taking water as a base fluid containing three different types of nanomaterials: copper (Cu), alumina (Al2O3), and titania (TiO2) under various scenarios based on the angle of the channels and Reynolds numbers. Accuracy and convergence of the designed scheme are validated through comparison with standard numerical results using the Adams method.
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Cigizoglu, H. Kerem, and Özgür Kişi. "Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data." Hydrology Research 36, no. 1 (February 1, 2005): 49–64. http://dx.doi.org/10.2166/nh.2005.0005.
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Flow forecasting performance by artificial neural networks (ANNs) is generally considered to be dependent on the data length. In this study k-fold partitioning, a statistical method, was employed in the ANN training stage. The method was found useful in the case of using the conventional feed-forward back propagation algorithm. It was shown that with a data period much shorter than the whole training duration similar flow prediction performance could be obtained. Prediction performance and convergence velocity comparison between three different back propagation algorithms, Levenberg–Marquardt, conjugate gradient and gradient descent was the next concern of the study and the Levenberg–Marquardt technique was found advantageous thanks to its shorter training duration and more satisfactory performance criteria.
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Awad, Asmaa J., Ahmed A. Ahmed, and Osamah F. Abdulateef. "Estimate and Analysis the Availability of Generator in Electric Power Plant Using ANN." Al-Khwarizmi Engineering Journal 18, no. 2 (June 16, 2022): 1–12. http://dx.doi.org/10.22153/kej.2022.04.001.
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The large number of failure in electrical power plant leads to the sudden stopping of work. In some cases, the necessary reserve materials are not available for maintenance which leads to interrupt of power generation in the electrical power plant unit. The present study, deals with the determination of availability aspects of generator in unit 5 of Al-Dourra electric power plant. In order to evaluate this generator's availability performance, a wide range of studies have been conducted to gather accurate information at the level of detail considered suitable to achieve the availability analysis aim. The Weibull Distribution is used to perform the reliability analysis via Minitab 17, and Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation. Operating data from the years 2015–2017 were used to calculate the availability by traditional method (Weibull distribution) and train the ANNs, while data from the year 2018 of operation were used to verify the model. The study implies that the ANN may be able to forecast the availability of the generator with a correlation coefficient (R) 0.99874 and a Mean Square Error (MSE) 5.6937E-06 between the availability predicted by ANN and Weibull distribution output.
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Bhyrapuneni, Srikanth, and Anandan Rajendran. "A Comparative Analysis for Optical Character Recognition for Text Extraction from Images Using Artificial Neural Network Fuzzy Inference System." Traitement du Signal 39, no. 1 (February 28, 2022): 283–89. http://dx.doi.org/10.18280/ts.390129.
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Artificial neural networks (ANN) has the capability to analyze raw data from processing input-output relationships. This function considers them important in areas of industry with such information is unusual. Researchers have tried to extract the information embedded within ANNs as set of rules used with inference systems to resolve the black-box function of ANNs. When ANN applied within a fuzzy inference system, the extracted rules yield high classification accuracy. In this paper a Multi-Layer Neural Feed-Forward Network using Artificial Neural Network Fuzzy Inference System (MLNFFN-ANNFIS) is proposed for accurate character recognition from images. The technique targets areas of business that have less complicated issues about which there is no simpler approach is desired to a complex one. This paper proposed an Optical Character Recognition model for Text Extraction from Images using Artificial Neural Network Fuzzy Inference System for accurate text detection from images. The technique proposed is more effective and simple than most of the techniques previously proposed. The proposed model is compared with various traditional models and the results indicate that the proposed model accuracy is more and performance is also improved.
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Costiris, N., E. Mavrommatis, K. A. Gernoth, and J. W. Clark. "A Global Model of β−-Decay Half-Lives Using Neural Networks." HNPS Proceedings 15 (January 1, 2020): 210. http://dx.doi.org/10.12681/hnps.2640.
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Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more re- cently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of β−-decay halflives of the class of nuclei that decay 100% by β− mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg- Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates gen- erated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the β−-decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart.
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Williamson, Roddy, and Abdul Chrachri. "A model biological neural network: the cephalopod vestibular system." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1479 (January 17, 2007): 473–81. http://dx.doi.org/10.1098/rstb.2006.1975.
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Artificial neural networks (ANNs) have become increasingly sophisticated and are widely used for the extraction of patterns or meaning from complicated or imprecise datasets. At the same time, our knowledge of the biological systems that inspired these ANNs has also progressed and a range of model systems are emerging where there is detailed information not only on the architecture and components of the system but also on their ontogeny, plasticity and the adaptive characteristics of their interconnections. We describe here a biological neural network contained in the cephalopod statocysts; the statocysts are analogous to the vertebrae vestibular system and provide the animal with sensory information on its orientation and movements in space. The statocyst network comprises only a small number of cells, made up of just three classes of neurons but, in combination with the large efferent innervation from the brain, forms an ‘active’ sense organs that uses feedback and feed-forward mechanisms to alter and dynamically modulate the activity within cells and how the various components are interconnected. The neurons are fully accessible to physiological investigation and the system provides an excellent model for describing the mechanisms underlying the operation of a sophisticated neural network.
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Gajamannage, K., D. I. Jayathilake, Y. Park, and E. M. Bollt. "Recurrent neural networks for dynamical systems: Applications to ordinary differential equations, collective motion, and hydrological modeling." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 1 (January 2023): 013109. http://dx.doi.org/10.1063/5.0088748.
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Classical methods of solving spatiotemporal dynamical systems include statistical approaches such as autoregressive integrated moving average, which assume linear and stationary relationships between systems’ previous outputs. Development and implementation of linear methods are relatively simple, but they often do not capture non-linear relationships in the data. Thus, artificial neural networks (ANNs) are receiving attention from researchers in analyzing and forecasting dynamical systems. Recurrent neural networks (RNNs), derived from feed-forward ANNs, use internal memory to process variable-length sequences of inputs. This allows RNNs to be applicable for finding solutions for a vast variety of problems in spatiotemporal dynamical systems. Thus, in this paper, we utilize RNNs to treat some specific issues associated with dynamical systems. Specifically, we analyze the performance of RNNs applied to three tasks: reconstruction of correct Lorenz solutions for a system with a formulation error, reconstruction of corrupted collective motion trajectories, and forecasting of streamflow time series possessing spikes, representing three fields, namely, ordinary differential equations, collective motion, and hydrological modeling, respectively. We train and test RNNs uniquely in each task to demonstrate the broad applicability of RNNs in the reconstruction and forecasting the dynamics of dynamical systems.
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MJOLSNESS, ERIC. "ON COOPERATIVE QUASI-EQUILIBRIUM MODELS OF TRANSCRIPTIONAL REGULATION." Journal of Bioinformatics and Computational Biology 05, no. 02b (April 2007): 467–90. http://dx.doi.org/10.1142/s0219720007002874.
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Mechanistic models for transcriptional regulation are derived using the methods of equilibrium statistical mechanics, to model equilibrating processes that occur at a fast time scale. These processes regulate slower changes in the synthesis and expression of transcription factors that feed back and cooperatively regulate transcription, forming a gene regulation network (GRN). We rederive and extend two previous quasi-equilibrium models of transcriptional regulation, and demonstrate circumstances under which they can be approximated at each transcription complex by feed-forward artificial neural network (ANN) models. A single-level mechanistic model can be approximated by a successfully applied phenomenological model of GRNs which is based on single-layer analog-valued ANNs. A two-level hierarchical mechanistic model, with separate activation states for modules and for the whole transcription complex, can be approximated by a two-layer feed-forward ANN in several related ways. The sufficient conditions demonstrated for the ANN approximations correspond biologically to large numbers of binding sites each of which have a small effect. A further extension to the single-level and two-level models allows one-dimensional chains of overlapping and/or energetically interacting binding sites within a module. Partition functions for these models can be constructed from stylized diagrams that indicate energetic and logical interactions between binary-valued state variables. All parameters in the mechanistic models, including the two approximations, can in principle be related to experimentally measurable free energy differences, among other observables.
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Yin, Ying, G. Y. Tian, Guo Fu Yin, and A. M. Luo. "Defect Identification and Classification for Digital X-Ray Images." Applied Mechanics and Materials 10-12 (December 2007): 543–47. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.543.
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Radiography inspection (X-ray or gamma ray) is one of the most commonly used Non-destructive Evaluation (NDE) methods. More and more digital X-ray imaging is used for medical diagnosis, security screening, or industrial inspection, which is important for e-manufacturing. In this paper, we firstly introduced an automatic welding defect inspection system for X-ray image evaluation, defect image database and applications of Artificial Neural Networks (ANNs) for NDE. Then, feature extraction and selection methods are used for defect representation. Seven categories of geometric features were defined and selected to represent characteristics of different kinds of welding defect. Finally, a feed-forward backpropagation neural network is implemented for the purpose of defect classification. The performance of the proposed methods are tested and discussed.
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KUNHIMANGALAM, REEDA, SUJITH OVALLATH, and PAUL K. JOSEPH. "ARTIFICIAL NEURAL NETWORKS IN THE IDENTIFICATION OF PERIPHERAL NERVE DISORDERS." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240018. http://dx.doi.org/10.1142/s0219519412400180.
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The recent years have witnessed an increase in the use of newer analytical tools in the field of medicine to assist in diagnostic procedure. Among the new tools, artificial neural networks (ANNs) have received particular attention because of their ability to analyze complex nonlinear data sets. This study suggests that ANNs can be used for the diagnosis of peripheral nerve disorders particularly the carpal tunnel syndrome (CTS) and neuropathy. This paper aims at building a classifier using a feed forward neural network that can distinguish between CTS, neuropathy, and normal controls using a reduced set of measurements or features from nerve conduction study (NCS) data. Three different ANN training algorithms, viz. Levenberg–Marquardt (LM), Conjugate gradient (CGB), and resilient back-propagation (RP) are used to see which algorithm produces better results and has faster training for the application under consideration. The data used were obtained from the Neurology Department, Kannur Medical College, Kerala, India. The obtained resultant confusion matrix indicated only a few misclassifications in all the three cases. The analysis showed that the CGB and RB algorithms provide faster convergence on pattern recognition problems, but the best performance in terms of accuracy is given by the LM algorithm. The accuracy obtained for the LM, CGB, and RB were 98.3%, 97.8%, and 97.2%, respectively. The respective sensitivities were 96.1%, 94.1%, and 94.1%, while the specificities were found to be equal to 99.4%, 98.8%, and 97.5%, respectively. The study aims at showing that ANNs may prove useful in combination with other systems in providing diagnostic and predictive medical opinions. However, it must always be kept in mind that ANNs represent only one form of computer-aided diagnosis, and the clinician's responsibility and overall control of patient care should never be underestimated.
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Zheng, Jin Xing, Ming Jun Zhang, and Qing Xin Meng. "Tool Cutting Force Modeling in High Speed Milling Using PSO-BP Neural Network." Key Engineering Materials 375-376 (March 2008): 515–19. http://dx.doi.org/10.4028/www.scientific.net/kem.375-376.515.
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The theory and the algorithm of Particle swarm optimization (PSO) based for neural network training were applied in the research of the modeling of milling force in high speed machining. The PSO was used to train connection weights of multi-layer feed forward neural network until the fitness error tended to be stable. Then BP algorithm was adopted to accomplish cutting force forecasting based on optimized initial weights, which take full use of the global optimization of PSO and local accurate searching of BP. The results of simulation showed that with comparison with other BP algorithms, PSO-BP not only effectively shortens the time of training networks, but also greatly improves the accuracy of prediction and universal approximation. PSO technique can act as an alternative training algorithm for ANNs.
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Ayrulu-Erdem, Birsel, and Billur Barshan. "Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals." Sensors 11, no. 2 (January 28, 2011): 1721–43. http://dx.doi.org/10.3390/s110201721.
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We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction.
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GHOSH, RANADHIR, JOHN YEARWOOD, MOUMITA GHOSH, and ADIL BAGIROV. "A HYBRID NEURAL LEARNING ALGORITHM USING EVOLUTIONARY LEARNING AND DERIVATIVE FREE LOCAL SEARCH METHOD." International Journal of Neural Systems 16, no. 03 (June 2006): 201–13. http://dx.doi.org/10.1142/s0129065706000615.
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In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.
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López-Aguilar, Kelvin, Adalberto Benavides-Mendoza, Susana González-Morales, Antonio Juárez-Maldonado, Pamela Chiñas-Sánchez, and Alvaro Morelos-Moreno. "Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter." Agriculture 10, no. 4 (April 1, 2020): 97. http://dx.doi.org/10.3390/agriculture10040097.
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Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.
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Robertson, Simon G., and Alexander K. Morison. "A trial of artificial neural networks for automatically estimating the age of fish." Marine and Freshwater Research 50, no. 1 (1999): 73. http://dx.doi.org/10.1071/mf98039.
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Artificial neural networks (ANNs) have the potential to automate routine ageing of fish with the benefit of increased speed in processing, greater objectivity and repeatability of estimates, and a mechanism for quantifying uncertainty of age estimates. ANN models were tested as a means of objectively replicating the age estimates of an experienced human reader. Feed-forward back- propagation ANNs, with three layers of neurons (input, hidden and output), were trained to classify the age of previously aged samples of three temperate species. Three ANN structures, where the number of neurons in the hidden layer was varied, were tested for each species. Inputs to each ANN were pixel brightness values along transects across images of sectioned otoliths. The ANN predicted age-class membership by the position of the neuron in the output layer with the highest value. After training, at least one of the three ANN structures correctly classified the age of fish from unseen transects for two members of the Sparidae family Acanthopagrus butcheri and Pagrus auratus at an accuracy level approaching that of an expert reader. For a member of the Merlucciidae family, Macruronus novaezelandiae, however, which is a species with more complex otolith structure, error rates were high for all three ANN structures tested.
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Nan, Dong Xiang, Yun Sheng Zhang, and Xue Qiang Sun. "Modeling of Proportional Integral Derivative Neural Networks Based on Quantum Computation." Advanced Materials Research 267 (June 2011): 757–61. http://dx.doi.org/10.4028/www.scientific.net/amr.267.757.
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There has been a growing interest in artificial neural networks (ANNs) based on quantum theoretical concepts and techniques due to cognitive science and computer science aspects. The so called Quantum Neural Networks (QNNs) are an exciting area of research in the field of quantum computation and quantum information. We proposed a modeling of Proportional integral derivative neural networks based on quantum computation called QNNs-PID that maps a nonlinear function. We analyze the main algorithms and architecture proposed the modeling of QNNs-PID. The main conclusion is that, up to now, we prove the feed back and back forward algorithm based on quantum computation how to use and give clearly the results for the nonlinear function in the context of QNNs-PID. We simulate an example to show the property of QNNs-PID in nonlinear systems.
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ALTUN, A. ALPASLAN, H. ERDINC KOCER, and NOVRUZ ALLAHVERDI. "GENETIC ALGORITHM BASED FEATURE SELECTION LEVEL FUSION USING FINGERPRINT AND IRIS BIOMETRICS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (May 2008): 585–600. http://dx.doi.org/10.1142/s0218001408006351.
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An accuracy level of unimodal biometric recognition system is not very high because of noisy data, limited degrees of freedom, spoof attacks etc. problems. A multimodal biometric system which uses two or more biometric traits of an individual can overcome such problems. We propose a multimodal biometric recognition system that fuses the fingerprint and iris features at the feature extraction level. A feed-forward artificial neural networks (ANNs) model is used for recognition of a person. There is a need to make the training time shorter, so the feature selection level should be performed. A genetic algorithms (GAs) approach is used for feature selection of a combined data. As an experiment, the database of 60 users, 10 fingerprint images and 10 iris images taken from each person, is used. The test results are presented in the last stage of this research.
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Umar, Muhammad, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Muhammad Shoaib, Manoj Gupta, and Yolanda Guerrero Sánchez. "A Stochastic Intelligent Computing with Neuro-Evolution Heuristics for Nonlinear SITR System of Novel COVID-19 Dynamics." Symmetry 12, no. 10 (October 2, 2020): 1628. http://dx.doi.org/10.3390/sym12101628.
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The present study aims to design stochastic intelligent computational heuristics for the numerical treatment of a nonlinear SITR system representing the dynamics of novel coronavirus disease 2019 (COVID-19). The mathematical SITR system using fractal parameters for COVID-19 dynamics is divided into four classes; that is, susceptible (S), infected (I), treatment (T), and recovered (R). The comprehensive details of each class along with the explanation of every parameter are provided, and the dynamics of novel COVID-19 are represented by calculating the solution of the mathematical SITR system using feed-forward artificial neural networks (FF-ANNs) trained with global search genetic algorithms (GAs) and speedy fine tuning by sequential quadratic programming (SQP)—that is, an FF-ANN-GASQP scheme. In the proposed FF-ANN-GASQP method, the objective function is formulated in the mean squared error sense using the approximate differential mapping of FF-ANNs for the SITR model, and learning of the networks is proficiently conducted with the integrated capabilities of GA and SQP. The correctness, stability, and potential of the proposed FF-ANN-GASQP scheme for the four different cases are established through comparative assessment study from the results of numerical computing with Adams solver for single as well as multiple autonomous trials. The results of statistical evaluations further authenticate the convergence and prospective accuracy of the FF-ANN-GASQP method.
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Diez, Francisco J., Ouiam F. Boukharta, Luis M. Navas-Gracia, Leticia Chico-Santamarta, Andrés Martínez-Rodríguez, and Adriana Correa-Guimaraes. "Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain." Sensors 22, no. 20 (October 13, 2022): 7772. http://dx.doi.org/10.3390/s22207772.
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In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.
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Osofisan, P. B., and J. O. Ilevbare. "Artificial Neural Network Approach for Solving Power Flow Problem: A Case Study of Ayede 132 KV Power System, Nigeria." Advanced Materials Research 367 (October 2011): 133–41. http://dx.doi.org/10.4028/www.scientific.net/amr.367.133.
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The main objective of this research work was to use Artificial Neural Network (ANN) based method for solving Power Flow Problem for a power system in Nigeria. This was achieved using the Backpropagation (multilayered feed-forward) Neural Network model. Two Backpropagation neural networks were designed and trained; one for computing voltage magnitudes on all buses and the other for computing voltage phase angles on all PV and PQ buses for different load and generation conditions for a 7-bus 132 kV power system in South-West Nigeria (Ayede). Due to unavailability of historical field records, data representing different scenarios of loading and/or generation conditions had to be generated using Newton-Raphson non-linear iterative method. A total of 250 scenarios were generated out of which 50% were used to train the ANNs, 25% were used for validation and the remaining 25% were used as test data for the ANNs. The test data results showed very high accuracy for the ANN used for computing voltage magnitudes for all test data with a Mean Square Error (MSE) of less than 10-6. Also, the ANN used for computing voltage phase angles showed very high accuracy in about 80% of the test data and acceptable results in about 97% of the test data. The MSE for all the test data results for the ANN computing voltage phase angles was less than 10-2.
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Gašperlin, M., F. Podlogar, and R. Šibanc. "Evolutionary Artificial Neural Networks as Tools for Predicting the Internal Structure of Microemulsions." Journal of Pharmacy & Pharmaceutical Sciences 11, no. 1 (March 24, 2008): 67. http://dx.doi.org/10.18433/j3f594.
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PURPOSE. The purpose of this study was to predict microemulsion structures by creating two artificial evolutionary neural networks (ANN) combined with a genetic algorithm. The first ANN would be able to determine the type of microemulsion from the desired composition, and the second to determine the type of microemulsion directly from a differential scanning calorimetry (DSC) curve. METHODS. The algorithms and the structures for each ANN were constructed and programmed in C++ computer language. The ANNs had a feed forward structure with one hidden level and were trained using a genetic algorithm. DSC was used to determine the microemulsion type. RESULTS. The ANNs showed very encouraging accuracy in predicting the microemulsion type from its composition and also directly from the DSC curve. The percentage success, calculated over the tested data, was over 90%. This enabled us, with satisfactory accuracy, to construct several pseudoternary diagrams that could facilitate the selection of the microemulsion composition to obtain the optimal desired drug carrier. CONCLUSIONS. The ANN constructed here, enhanced with a genetic algorithm, is an effective tool for predicting the type of microemulsion. These findings provide the basis for reducing research time and development cost for characterizing microemulsion properties. Its application would stimulate the further development of such colloidal drug delivery systems, exploit their advantages and, to a certain extent, avoid their disadvantages.
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Iliadis, Lazaros, Shawn D. Mansfield, Stavros Avramidis, and Yousry A. El-Kassaby. "Predicting Douglas-fir wood density by artificial neural networks (ANN) based on progeny testing information." Holzforschung 67, no. 7 (October 1, 2013): 771–77. http://dx.doi.org/10.1515/hf-2012-0132.
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Abstract A heuristic wood density prediction model has been developed by means of artificial neural networks (ANNs). Four populations of 32-year-old coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesi) trees representing 20 full-sib families growing on comparable sites were in focus of this study. Tree height, diameter, volume, wood density, and acoustic velocity data from 632 trees were considered for the calculations. Two different ANN platforms were developed employing different classes and architectures, namely, the multilayer feed-forward (MLFF) and modular (MOD) models. After establishing the optimal configuration of the model, a MLFF network and a MOD neural network (with the obtained optimal structure) were developed and tested without cross-validation by employing a typical training and testing set methodology. To this purpose, the data set was divided in 480 trees for training and 152 trees for validation. A significant relationship between actual and predicted wood density was obtained with R2 values of 0.50 and 0.52 for the two networks, respectively, demonstrating their predictive potential for wood density estimation. A classic multiple regression analysis produced substantially lower predictive power with an R2 of 0.23. The application of ANNs as a viable predictive tool in determining wood density using growth and acoustic velocity data without additional intrusive sampling and laboratory work was demonstrated. An additional work including other species is required for these approaches.
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Jiménez-Macías, Emilio, Angel Sánchez-Roca, Hipólito Carvajal-Fals, Julio Blanco-Fernández, and Eduardo Martínez-Cámara. "Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN-Based Model." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/728564.
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This paper analyses the correlation between the acoustic emission signals and the main parameters of friction stir welding process based on artificial neural networks (ANNs). The acoustic emission signals inZandYdirections have been acquired by the AE instrument NI USB-9234. Statistical and temporal parameters of discomposed acoustic emission signals using Wavelet Transform have been used as input of the ANN. The outputs of the ANN model include the parameters of tool rotation speed and travel speed, and tool profile, as well as the tensile strength. A multilayer feed-forward neural network has been selected and trained, using Levenberg-Marquardt algorithm for different network architectures. Finally, an analysis of the comparison between the measured and the calculated data is presented. The model obtained can be used to model and develop an automatic control of the parameters of the process and mechanical properties of joint, based on the acoustic emission signals.
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ELRAGAL, HASSAN M. "IMPROVING NEURAL NETWORKS PREDICTION ACCURACY USING PARTICLE SWARM OPTIMIZATION COMBINER." International Journal of Neural Systems 19, no. 05 (October 2009): 387–93. http://dx.doi.org/10.1142/s0129065709002099.
Full textAbstract:
This paper proposes a technique to improve Artificial Neural Network (ANN) prediction accuracy using Particle Swarm Optimization (PSO) combiner. A hybrid system consists of two stages with the first stage containing two ANNs. The first ANN predictor is a multi-layer feed-forward network trained with error back-propagation and the second predictor is a functional link network. These two predictors are combined in the second stage using PSO combiner in a linear and non-linear fashion. The proposed method is applied to problem of predicting daily natural gas consumption. The performance of ANN predictors and combination methods are tested on real data from four different gas utilities. The experimental results show that the proposed particle swarm optimization combiners results in more accurate prediction compared to using single ANN predictor. Prediction accuracy improvement of the proposed PSO combiners have been shown using hypothesis testing.
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Goodacre, Royston. "Use of Pyrolysis Mass Spectrometry with Supervised Learning for the Assessment of the Adulteration of Milk of Different Species." Applied Spectroscopy 51, no. 8 (August 1997): 1144–53. http://dx.doi.org/10.1366/0003702971941665.
Full textAbstract:
Binary mixtures of 0–20% cows' milk with ewes' milk, 0–20% cows' milk with goats' milk, and 0–5% cows' milk with goats' milk were subjected to pyrolysis mass spectrometry (PyMS). For analysis of the pyrolysis mass spectra so as to determine the percentage adulteration of either caprine or ovine milk with bovine milk, partial least-squares regression (PLS), principal components regression (PCR) and fully interconnected feed-forward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified by using the standard back-propagation algorithm, and the nodes used a sigmoidal squashing function. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the percentage adulteration with cows' milk to <1% for samples, with an accuracy of ±0.5%, on which they had not been trained. Scaling the individual nodes on the input layer of ANNs significantly decreased the time taken for the ANNs to learn, compared with scaling across the whole mass range; however in one case this approach resulted in poor generalization for the estimates of percentage cows' milk in ewes' milk. To assess whether the calibration models had learned the differences between the milk species or the differences due to the different fat content of in each of the milk types, we also analyzed pure milk samples varying in fat content by PyMS. Cluster analysis showed unequivocally that the major variation between the different milk species was not due to variable fat content. Since any biological material can be pyrolyzed in this way, the combination of PyMS with supervised learning constitutes a rapid, powerful, and novel approach to the quantitative assessment of food adulteration generally.
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Özdoğan, Hasan, Yiğit Ali Üncü, Mert Şekerci, and Abdullah Kaplan. "A study on the estimations of (n, t) reaction cross-sections at 14.5 MeV by using artificial neural network." Modern Physics Letters A 36, no. 23 (July 30, 2021): 2150168. http://dx.doi.org/10.1142/s0217732321501686.
Full textAbstract:
In this paper, calculations of the [Formula: see text] reaction cross-sections at 14.5 MeV have been presented by utilizing artificial neural network algorithms (ANNs). The systematics are based on the account for the non-equilibrium reaction mechanism and the corresponding analytical formulas of the pre-equilibrium exciton model. Experimental results, obtained from the EXFOR database, have been used to train the ANN with the Levenberg–Marquardt (LM) algorithm which is a feed-forward algorithm and is considered one of the well-known and most effective methods in neural networks. The Regression [Formula: see text] values for the ANN estimation have been determined as 0.9998, 0.9927 and 0.9895 for training, testing and for all process. The [Formula: see text] reaction cross-sections have been reproduced with the TALYS 1.95 and the EMPIRE 3.2 codes. In summary, it has been demonstrated that the ANN algorithms can be used to calculate the [Formula: see text] reaction cross-section with the semi-empirical systematics.