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2014 State Estimation for Discrete-Time Stochastic Neural Networks with Mixed Delays
Liyuan Hou, Hong Zhu, Shouming Zhong, Yong Zeng, Lin Shi
J. Appl. Math. 2014: 1-14 (2014). DOI: 10.1155/2014/209486


This paper investigates the analysis problem for stability of discrete-time neural networks (NNs) with discrete- and distribute-time delay. Stability theory and a linear matrix inequality (LMI) approach are developed to establish sufficient conditions for the NNs to be globally asymptotically stable and to design a state estimator for the discrete-time neural networks. Both the discrete delay and distribute delays employ decomposing the delay interval approach, and the Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals, such that a new stability criterion is proposed in terms of linear matrix inequalities (LMIs). Numerical examples are given to demonstrate the effectiveness of the proposed method and the applicability of the proposed method.


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Liyuan Hou. Hong Zhu. Shouming Zhong. Yong Zeng. Lin Shi. "State Estimation for Discrete-Time Stochastic Neural Networks with Mixed Delays." J. Appl. Math. 2014 1 - 14, 2014.


Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 1294.30082
MathSciNet: MR3173326
Digital Object Identifier: 10.1155/2014/209486

Rights: Copyright © 2014 Hindawi


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