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A New Neural Network Training Algorithm Based on Artificial Bee Colony Algorithm for Nonlinear System Identification

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  • Ebubekir Kaya

    (Department of Computer Engineering, Engineering Architecture Faculty, Nevsehir Haci Bektas Veli Univesity, 50300 Nevsehir, Turkey)

Abstract

Artificial neural networks (ANNs), one of the most important artificial intelligence techniques, are used extensively in modeling many types of problems. A successful training process is required to create effective models with ANN. An effective training algorithm is essential for a successful training process. In this study, a new neural network training algorithm called the hybrid artificial bee colony algorithm based on effective scout bee stage (HABCES) was proposed. The HABCES algorithm includes four fundamental changes. Arithmetic crossover was used in the solution generation mechanisms of the employed bee and onlooker bee stages. The knowledge of the global best solution was utilized by arithmetic crossover. Again, this solution generation mechanism also has an adaptive step size. Limit is an important control parameter. In the standard ABC algorithm, it is constant throughout the optimization. In the HABCES algorithm, it was determined dynamically depending on the number of generations. Unlike the standard ABC algorithm, the HABCES algorithm used a solution generation mechanism based on the global best solution in the scout bee stage. Through these features, the HABCES algorithm has a strong local and global convergence ability. Firstly, the performance of the HABCES algorithm was analyzed on the solution of global optimization problems. Then, applications on the training of the ANN were carried out. ANN was trained using the HABCES algorithm for the identification of nonlinear static and dynamic systems. The performance of the HABCES algorithm was compared with the standard ABC, aABC and ABCES algorithms. The results showed that the performance of the HABCES algorithm was better in terms of solution quality and convergence speed. A performance increase of up to 69.57% was achieved by using the HABCES algorithm in the identification of static systems. This rate is 46.82% for the identification of dynamic systems.

Suggested Citation

  • Ebubekir Kaya, 2022. "A New Neural Network Training Algorithm Based on Artificial Bee Colony Algorithm for Nonlinear System Identification," Mathematics, MDPI, vol. 10(19), pages 1-27, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3487-:d:923875
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    References listed on IDEAS

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    1. Balasubbareddy Mallala & Venkata Prasad Papana & Ravindra Sangu & Kowstubha Palle & Venkata Krishna Reddy Chinthalacheruvu, 2022. "Multi-Objective Optimal Power Flow Solution Using a Non-Dominated Sorting Hybrid Fruit Fly-Based Artificial Bee Colony," Energies, MDPI, vol. 15(11), pages 1-16, June.
    2. Ebubekir Kaya, 2022. "A Comprehensive Comparison of the Performance of Metaheuristic Algorithms in Neural Network Training for Nonlinear System Identification," Mathematics, MDPI, vol. 10(9), pages 1-25, May.
    3. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
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    2. Sherif A. Zaid & Ahmed M. Kassem & Aadel M. Alatwi & Hani Albalawi & Hossam AbdelMeguid & Atef Elemary, 2023. "Optimal Control of an Autonomous Microgrid Integrated with Super Magnetic Energy Storage Using an Artificial Bee Colony Algorithm," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    3. Yunshan Sun & Qian Huang & Ting Liu & Yuetong Cheng & Yanqin Li, 2023. "Multi-Strategy Enhanced Harris Hawks Optimization for Global Optimization and Deep Learning-Based Channel Estimation Problems," Mathematics, MDPI, vol. 11(2), pages 1-28, January.
    4. Marjan Golob, 2023. "NARX Deep Convolutional Fuzzy System for Modelling Nonlinear Dynamic Processes," Mathematics, MDPI, vol. 11(2), pages 1-22, January.

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