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

Lijie Geng | Zendy; Haiying Li | Zendy; Bingchen Zhao | Zendy; Guang Su | Zendy

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State Estimation for Discrete-Time Fuzzy Cellular Neural Networks with Mixed Time Delays

Author(s) -

Lijie Geng,

Haiying Li,

Bingchen Zhao,

Guang Su

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/906439

Subject(s) - estimator , discrete time and continuous time , artificial neural network , computer science , state (computer science) , fuzzy logic , matlab , matrix (chemical analysis) , exponential stability , term (time) , mathematics , algorithm , control theory (sociology) , mathematical optimization , artificial intelligence , nonlinear system , statistics , materials science , physics , control (management) , quantum mechanics , composite material , operating system

This paper is concerned with the exponential state estimation problem for a class of discrete-time fuzzy cellular neural networks with mixed time delays. The main purpose is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable. By constructing a novel Lyapunov-Krasovskii functional which contains a triple summation term, some sufficient conditions are derived to guarantee the existence of the state estimator. The linear matrix inequality approach is employed for the first time to deal with the fuzzy cellular neural networks in the discrete-time case. Compared with the present conditions in the form of M-matrix, the results obtained in this paper are less conservative and can be checked readily by the MATLAB toolbox. Finally, some numerical examples are given to demonstrate the effectiveness of the proposed results

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