Research on Asymptotic Stability for Markovian Jumping Neural Network with Unknown Transition Probabilities

doi:10.16182/j.issn1004731x.joss.201702001

Abstract

Abstract: The analysis problem of asymptotic stability for a class of uncertain neural networks with Markovian jumping parameters and time delays was addressed. The general representative dynamic stochastic neural network model was established. The considered transition probabilities were assumed to be partially unknown. The parameter uncertainties were considered to be norm-bounded. Based on Lyapunov stability theory, by constructing a suitable Lyapunov-Krasovskii function and using the stochastic analysis method, some sufficient criteria for the stability of discrete Markovian neural networks was derived. Through the Matlab LMI toolbox, solving a set of linear matrix inequalities to test criterion, the new criterion reduced the conservatism of the results. A numerical example illustrates the effectiveness of the proposed theory.

Key words: neural networks, Markovian jumping parameters, partly unknown transition probabilities, asymptotic stability

CLC Number:

TP391.9

Lu Yang, Yi Shujuan, Ren Weijian, Liu Jiandong. Research on Asymptotic Stability for Markovian Jumping Neural Network with Unknown Transition Probabilities[J]. Journal of System Simulation, 2017, 29(2): 235-240.

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