pth Moment Exponential Stability of Stochastic Recurrent Neural Networks with Markovian Switching

Authors:
Enwen Zhu;Quan Yuan
Affiliations:
School of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, China 410004;Department of Mathematics, Wayne State University, Detroit, USA 48202
Venue:
Neural Processing Letters
Year:
2013

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Abstract

This paper investigates the problem of the pth moment exponential stability for a class of stochastic recurrent neural networks with Markovian jump parameters. With the help of Lyapunov function, stochastic analysis technique, generalized Halanay inequality and Hardy inequality, some novel sufficient conditions on the pth moment exponential stability of the considered system are derived. The results obtained in this paper are completely new and complement and improve some of the previously known results (Liao and Mao, Stoch Anal Appl, 14:165---185, 1996; Wan and Sun, Phys Lett A, 343:306---318, 2005; Hu et al., Chao Solitions Fractals, 27:1006---1010, 2006; Sun and Cao, Nonlinear Anal Real, 8:1171---1185, 2007; Huang et al., Inf Sci, 178:2194---2203, 2008; Wang et al., Phys Lett A, 356:346---352, 2006; Peng and Liu, Neural Comput Appl, 20:543---547, 2011). Moreover, a numerical example is also provided to demonstrate the effectiveness and applicability of the theoretical results.