Stability analysis for discrete-time Markovian jump neural networks with mixed time-delays

Authors:
Zheng-Guang Wu;Peng Shi;Hongye Su;Jian Chu
Affiliations:
National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang 310027, PR China;Department of Computing and Mathematical Sciences, University of Glamorgan, Pontypridd CF37 1DL, UK and School of Engineering and Science, Victoria University, Melbourne, 8001 Vic, Australia;National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang 310027, PR China;National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang 310027, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

The problem of delay-dependent stability analysis is investigated for discrete-time Markovian jump neural networks with mixed time-delays (both discrete and infinity-distributed time delays). The Markov chain in the underlying neural networks is finite piecewise homogeneous. A delay-dependent condition is derived for the addressed neural networks to be globally asymptotically stable. As an extension, we further consider the stability analysis problem for the same type of neural networks but with partially known transition probabilities. Two numerical examples are given to demonstrate the usefulness of the derived methods.