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

Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi and Kim Gaik Tay, Kim Gaik Tay and Audrey Huong, Audrey Huong and Saratha Sathasivam, Saratha Sathasivam and Majid Khan bin Majahar Ali, Majid Khan bin Majahar Ali (2023) Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms. Computer Systems Science and Engineering, 47 (1). pp. 1163-1184.

Text J16174_ee1fefba9e830abb0e36ae31d95d9997.pdf Restricted to Registered users only Download (1MB) | Request a copy

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	: Satisfiability logic programming; symbolic radial basis function neural network; evolutionary programming algorithm; genetic algorithm; evolution strategy algorithm; differential evolution algorithm
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	03 Jan 2024 01:34
Last Modified:	03 Jan 2024 01:34
URI:	http://eprints.uthm.edu.my/id/eprint/10524

Actions (login required)

View Item