Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 20, Issue 5, Pages 840-855Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2009.2015085
Keywords
Almost sure exponential stability; Halanay inequality; Markov chain; recurrent neural networks; time-varying delay
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Funding
- Research Grants Council of the Hong Kong Special Administrative Region, China [CUHK417608E]
- Natural Science Foundation of China [60574025, 60740430664, 60874031]
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This paper presents new stability results for recurrent neural networks with Markovian switching. First, algebraic criteria for the almost sure exponential stability of recurrent neural networks with Markovian switching and without time delays are derived. The results show that the almost sure exponential stability of such a neural network does not require the stability of the neural network at every individual parametric configuration. Next, both delay-dependent and delay-independent criteria for the almost sure exponential stability of recurrent neural networks with time-varying delays and Markovian-switching parameters are derived by means of a generalized stochastic Halanay inequality. The results herein include existing ones for recurrent neural networks without Markovian switching as special cases. Finally, simulation results in three numerical examples are discussed to illustrate the theoretical results.
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