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

Liyuan Hou | Zendy; Hong Zhu | Zendy; Shouming Zhong | Zendy; Yong Zeng | Zendy; Lin Shi | Zendy

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

Author(s) -

Liyuan Hou,

Hong Zhu,

Shouming Zhong,

Yong Zeng,

Lin Shi

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/209486

Subject(s) - discrete time and continuous time , artificial neural network , estimator , linear matrix inequality , mathematics , stability (learning theory) , control theory (sociology) , interval (graph theory) , state estimator , stability theory , state (computer science) , matrix (chemical analysis) , exponential stability , computer science , mathematical optimization , algorithm , nonlinear system , control (management) , artificial intelligence , statistics , physics , materials science , combinatorics , machine learning , quantum mechanics , composite material

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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