Global exponential stability analysis of fuzzy BAM neural networks with time-varying delays

Article type: Research Article

Authors: Lou, Xuyang^{; *} | Cui, Baotong

Affiliations: School of Communication and Control Engineering, Southern Yangtze University, 1800 Lihu Rd., Wuxi, Jiangsu 214122, P.R. China

Correspondence: [*] Corresponding author. E-mail: louxuyang28945@163.com; btcui@vip.sohu.com

Abstract: In this paper, the stability problem of Takagi-Sugeno fuzzy BAM neural networks with time-varying delays is considered. Firstly, this model is established as a modified Takagi-Sugeno fuzzy model in which the consequent parts are composed of a set of BAM neural networks with time-varying delays. Secondly, a globally exponentially stable condition is presented in terms of linear matrix inequalities, which can be easily solved by some standard numerical packages. Finally, two numerical examples are given to illustrate the effectiveness of the theoretical results.

Keywords: Fuzzy BAM neural networks, global exponential stability, time-varying delay, linear matrix inequality

DOI: 10.3233/HIS-2007-4203

Journal: International Journal of Hybrid Intelligent Systems, vol. 4, no. 2, pp. 89-101, 2007