Khaledi, Omid, Sadodin, Seyfolah, Rostamian, Seyed Hadi. (1401). Optimization of the nonlinear model of neural network training in predicting thermal efficiency of solar concentrator with simulated annealing algorithm. هنرهای کاربردی, 13(2), 2947-2960. doi: 10.22075/ijnaa.2021.22254.2341
Omid Khaledi; Seyfolah Sadodin; Seyed Hadi Rostamian. "Optimization of the nonlinear model of neural network training in predicting thermal efficiency of solar concentrator with simulated annealing algorithm". هنرهای کاربردی, 13, 2, 1401, 2947-2960. doi: 10.22075/ijnaa.2021.22254.2341
Khaledi, Omid, Sadodin, Seyfolah, Rostamian, Seyed Hadi. (1401). 'Optimization of the nonlinear model of neural network training in predicting thermal efficiency of solar concentrator with simulated annealing algorithm', هنرهای کاربردی, 13(2), pp. 2947-2960. doi: 10.22075/ijnaa.2021.22254.2341
Khaledi, Omid, Sadodin, Seyfolah, Rostamian, Seyed Hadi. Optimization of the nonlinear model of neural network training in predicting thermal efficiency of solar concentrator with simulated annealing algorithm. هنرهای کاربردی, 1401; 13(2): 2947-2960. doi: 10.22075/ijnaa.2021.22254.2341

Optimization of the nonlinear model of neural network training in predicting thermal efficiency of solar concentrator with simulated annealing algorithm

International Journal of Nonlinear Analysis and Applications
مقاله 236، دوره 13، شماره 2، مهر 2022، صفحه 2947-2960    اصل مقاله (1.28 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.22254.2341
نویسندگان
Omid Khaledi1؛ Seyfolah Sadodin* 2؛ Seyed Hadi Rostamian* 2
1Faculty of Mechanical Engineering, Semnan University, Semnan, Iran
2School of Engineering, Damghan University, Damghan, Iran
چکیده
Nowadays, artificial neural networks are widely used to solve large-scale and complex problems. The purpose of this study is to use artificial intelligence techniques such as artificial neural networks and simulated annealing algorithm, to optimize the solar energy system in order to maximize its economic benefits. Here, a new nonlinear computational model has been presented to predict the thermal performance of compound parabolic concentrator (CPC). In this regard, three models of artificial neural network (ANN) including radial-basis function (RBFANN), multi-layer perception (MLPANN) as well as adaptive neuro fuzzy inference system (ANFIS) are used to identify the nonlinear relationship between input and output parameters of the system. The optimal nonlinear structure of the model is determined through the simulated annealing (SA) method. Validation of the proposed models is performed on a CPC. The results show that all the three models are efficient. In particular, statistical analyses show that the ANFIS model is more accurate in the process of predicting thermal efficiency. So, the given models can be effectively applied in practical fields.
کلیدواژه‌ها
neural network؛ nonlinear optimization؛ simulated annealing algorithm؛ compound parabolic concentrator؛ thermal efficiency
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