An online self-adaptive RBF network algorithm based on the Levenberg-Marquardt algorithm

Zhang, ZhaoZhao and Liu, Yue and Zhu, YingQin and Zhao, XiaoFei (2022) An online self-adaptive RBF network algorithm based on the Levenberg-Marquardt algorithm. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514

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Abstract

Aiming at the problem that the Levenberg-Marquardt (LM) algorithm can not train online radial basis function (RBF) neural network and the deficiency in the RBF network structure design methods, this paper proposes an online self-adaptive algorithm for constructing RBF neural network (OSA-RBFNN) based on LM algorithm. Thus, the ideas of the sliding window method and online structure optimization methods are adopted to solve the proposed problems. On the one hand, the sliding window method enables the RBF network to be trained online by the LM algorithm making the RBF network more robust to the changes in the learning parameters and faster convergence compared with the other investigated algorithms. On the other hand, online structure optimization can adjust the structure of the RBF network based on the information of training errors and hidden nodes to track the non-linear time-varying systems, which helps to maintain a compact network and satisfactory generalization ability. Finally, verified by simulation analysis, it is demonstrated that OSA-RBFNN exhibits a compact RBF network.

Item Type: Article
Subjects: Open Research Librarians > Computer Science
Depositing User: Unnamed user with email support@open.researchlibrarians.com
Date Deposited: 16 Jun 2023 10:09
Last Modified: 02 Nov 2023 06:25
URI: http://stm.e4journal.com/id/eprint/1227

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