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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN)...

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Main Authors: Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan
Format: Article
Language:English
Published: 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/10078/1/J16174_ee1fefba9e830abb0e36ae31d95d9997.pdf
http://eprints.uthm.edu.my/10078/
http://dx.doi.org/10.32604/csse.2023.038912
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spelling my.uthm.eprints.100782023-10-11T03:24:06Z http://eprints.uthm.edu.my/10078/ Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi Kim Gaik Tay, Kim Gaik Tay Audrey Huong, Audrey Huong Saratha Sathasivam, Saratha Sathasivam Majahar Ali, Majid Khan T Technology (General) Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior’s integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN2SAT). The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language. With the use of SRBFNN-2SAT, a training method based on these algorithms has been presented, then training has been compared among algorithms, which were applied in Microsoft Visual C++ software using multiple metrics of performance, including Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Systematic Error (SD), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight, accompanied by the slightest iteration error, which minimizes the objective function of SRBFNN-2SAT. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10078/1/J16174_ee1fefba9e830abb0e36ae31d95d9997.pdf Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi and Kim Gaik Tay, Kim Gaik Tay and Audrey Huong, Audrey Huong and Saratha Sathasivam, Saratha Sathasivam and Majahar Ali, Majid Khan (2023) Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms. Computer Systems Science and Engineering, 47 (1). pp. 1163-1184. http://dx.doi.org/10.32604/csse.2023.038912
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi
Kim Gaik Tay, Kim Gaik Tay
Audrey Huong, Audrey Huong
Saratha Sathasivam, Saratha Sathasivam
Majahar Ali, Majid Khan
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
description Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior’s integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN2SAT). The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language. With the use of SRBFNN-2SAT, a training method based on these algorithms has been presented, then training has been compared among algorithms, which were applied in Microsoft Visual C++ software using multiple metrics of performance, including Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Systematic Error (SD), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight, accompanied by the slightest iteration error, which minimizes the objective function of SRBFNN-2SAT.
format Article
author Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi
Kim Gaik Tay, Kim Gaik Tay
Audrey Huong, Audrey Huong
Saratha Sathasivam, Saratha Sathasivam
Majahar Ali, Majid Khan
author_facet Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi
Kim Gaik Tay, Kim Gaik Tay
Audrey Huong, Audrey Huong
Saratha Sathasivam, Saratha Sathasivam
Majahar Ali, Majid Khan
author_sort Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi
title Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
title_short Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
title_full Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
title_fullStr Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
title_full_unstemmed Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
title_sort evolution performance of symbolic radial basis function neural network by using evolutionary algorithms
publishDate 2023
url http://eprints.uthm.edu.my/10078/1/J16174_ee1fefba9e830abb0e36ae31d95d9997.pdf
http://eprints.uthm.edu.my/10078/
http://dx.doi.org/10.32604/csse.2023.038912
_version_ 1779440620225429504
score 13.211869

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