Journal of Applied Mathematics

State Estimation for Discrete-Time Stochastic Neural Networks with Mixed Delays

Liyuan Hou, Hong Zhu, Shouming Zhong, Yong Zeng, and Lin Shi

Full-text: Open access

Abstract

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.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 209486, 14 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305532

Digital Object Identifier
doi:10.1155/2014/209486

Mathematical Reviews number (MathSciNet)
MR3173326

Zentralblatt MATH identifier
1294.30082

Citation

Hou, Liyuan; Zhu, Hong; Zhong, Shouming; Zeng, Yong; Shi, Lin. State Estimation for Discrete-Time Stochastic Neural Networks with Mixed Delays. J. Appl. Math. 2014 (2014), Article ID 209486, 14 pages. doi:10.1155/2014/209486. https://projecteuclid.org/euclid.jam/1425305532


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