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Extended dissipative state estimation for uncertain discrete-time Markov jump neural networks with mixed time delays
Qian Li1, Qingxin Zhu1, Shouming Zhong2
1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China.
Abstract:
This paper is concerned with the problem of extended dissipativity-based state estimation for uncertain discrete-time Markov jump neural networks with finite piecewise homogeneous Markov chain and mixed time delays. The aim of this paper is to present a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative. A triple-summable term is introduced in the constructed Lyapunov function and the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term. The extended dissipativity criterion is derived in form of linear matrix inequalities. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.
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