On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems

Abstract

Artificial neural networks (ANNs) are used to solve many problems, such as modeling, identification, prediction, and classification. The success of ANN is directly related to the training process. Meta-heuristic algorithms are used extensively for ANN training. Within the scope of this study, a feed-forward artificial neural network (FFNN) is trained using the marine predators algorithm (MPA), one of the current meta-heuristic algorithms. Namely, this study is aimed to evaluate the performance of MPA in ANN training in detail. Identification/modeling of nonlinear systems is chosen as the problem. Six nonlinear systems are used in the applications. Some of them are static, and some are dynamic. Mean squared error (MSE) is utilized as the error metric. Effective training and testing results were obtained using MPA. The best mean error values obtained for six nonlinear systems are 2.3 × 10−4, 1.8 × 10−3, 1.0 × 10−4, 1.0 × 10−4, 1.2 × 10−5, and 2.5 × 10−4. The performance of MPA is compared with 16 meta-heuristic algorithms. The results have shown that the performance of MPA is better than other algorithms in ANN training for the identification of nonlinear systems.

Deni̇z Yirticilari Algori̇tmasi İle Türki̇ye’ni̇n Enerji̇ Talebi̇ni̇n Tahmi̇n Edi̇lmesi̇ne Yöneli̇k Kapsamli Bi̇r Çalişma

“…MPA, 2020 yılında önerilmiş güçlü bir metasezgisel algoritmadır. Yapısında barındırdığı üç farklı arama stratejisi sayesinde benchmark problemlerinin optimizasyonunda (Arslan, 2023), yapay sinir ağlarının eğitilmesinde (Baştemur Kaya, 2023;Ikram vd., 2022), öznitelik seçim problemlerinde (Abd Elminaam vd., 2021), görüntü işleme problemlerinde (Abdel-Basset vd., 2022), parametre tahmini problemlerinde (Abdel-Basset vd., 2021) ve çok amaçlı problemlerin çözümünde (Jangir vd., 2023;Zhong vd., 2021) başarılı olduğu görülmüştür.…”