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Open AccessArticle

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
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Processes 2020, 8(10), 1295; https://doi.org/10.3390/pr8101295
Received: 22 July 2020 / Revised: 17 September 2020 / Accepted: 22 September 2020 / Published: 16 October 2020
(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems)
A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE was rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms. View Full-Text
Keywords: artificial immune system; differential evolution; genetic algorithm; artificial bee colony; particle swarm optimization; radial basis functions neural network; 2-satisfiability based reverse analysis artificial immune system; differential evolution; genetic algorithm; artificial bee colony; particle swarm optimization; radial basis functions neural network; 2-satisfiability based reverse analysis
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Alzaeemi, S.A.; Sathasivam, S. Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network. Processes 2020, 8, 1295.

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