Adaptive Nonlinear Systems Identification via Discrete Multi-Time Scales Dynamic Neural Networks. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Xie, Wei-Dong; Fu, Zhi-Jun; Xie, Wen-Fang
• Is Part Of: Intelligent automation & soft computing. Volume 22:Issue 1(2016)
• Page Start: 111
• Page End: 123
• Publication Date: 2016-01-02
• Subjects: Nonliear identification -- Neural Network -- Multi-Time scales; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2015.1058469
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: In this paper, we extend our previous results on continuous multi-time scales dynamic neural networks identification to the discrete domain. A robust on-line identification algorithm is proposed for nonlinear systems identification via discrete multi-time scales dynamic neural networks. The main contribution of the paper is that the input-to-state stability (ISS) approach is used to tune the weights of the discrete multi-time scales dynamic neural networks in the sense of L1. The commonly used robustifying techniques, such as dead-zone or s -modification in the weight tuning, are not necessary for the proposed identification algorithm. The stability of the proposed identifier is proved by Lyapunov function and ISS theory. Two examples are given to demonstrate the correctness of the theoretical results.