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