WCSE 2017
ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.205

Identification of Nonlinear System Based on Additive Legendre Neural Network

Bin Yang

Abstract— In this paper, an efficient identification method based on additive Legendre neural network (ALNN) model and hybrid evolutionary method is proposed to identify nonlineat systems. In order to improve efficiency of Legendre neural network (LNN), additive Legendre neural network is proposed. For finding the optimal structure and parameters of ALNN model, a new hybrid evolutionary method besed on binary particle swarm optimization (BPSO) algorithm and firefly algorithm is employed. Two nonlinear system identification experiments are used to test ALNN model. The results reveal that ALNN model performs better than LNN and other classic neural networks.

Index Terms— additive, Legendre neural network, binary particle swarm optimization, firefly algorithm.

Bin Yang
School of Information Science and Engineering, Zaozhuang University, CHINA

ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.17Xsrc="http://www.wcse.org/uploadfile/2019/0823/20190823055609629.png" style="width: 120px; height: 68px;" />[Download]

Cite: Bin Yang, "Identification of Nonlinear System Based on Additive Legendre Neural Network," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 1174-1178, Beijing, 25-27 June, 2017.