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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| Format: | Article |
| Language: | English |
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Tech Science Press
2023
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| Online Access: | http://eprints.uthm.edu.my/9646/1/J16174_ee1fefba9e830abb0e36ae31d95d9997.pdf |
| _version_ | 1825710805591523328 |
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| author | Alzaeemi, Shehab Abdulhabib Kim Gaik Tay, Kim Gaik Tay Audrey Huong, Audrey Huong Saratha Sathasivam, Saratha Sathasivam Majahar Ali, Majid Khan |
| author_facet | Alzaeemi, Shehab Abdulhabib Kim Gaik Tay, Kim Gaik Tay Audrey Huong, Audrey Huong Saratha Sathasivam, Saratha Sathasivam Majahar Ali, Majid Khan |
| author_sort | Alzaeemi, Shehab Abdulhabib |
| collection | UTHM |
| 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. |
| first_indexed | 2024-03-05T22:03:13Z |
| format | Article |
| id | uthm.eprints-9646 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:03:13Z |
| publishDate | 2023 |
| publisher | Tech Science Press |
| record_format | dspace |
| spelling | uthm.eprints-96462023-08-16T07:10:42Z http://eprints.uthm.edu.my/9646/ Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms Alzaeemi, Shehab Abdulhabib 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. Tech Science Press 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9646/1/J16174_ee1fefba9e830abb0e36ae31d95d9997.pdf Alzaeemi, Shehab Abdulhabib 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. https://doi.org/10.32604/csse.2023.038912 |
| spellingShingle | T Technology (General) Alzaeemi, Shehab Abdulhabib 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 |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9646/1/J16174_ee1fefba9e830abb0e36ae31d95d9997.pdf |
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