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

Authors

Lu Yang, 1. College of Information and Technology, Heilongjiang Bayi Agricultural University, Daqing 163319, China; ;
Shujuan Yi, 1. College of Information and Technology, Heilongjiang Bayi Agricultural University, Daqing 163319, China; ;
Weijian Ren, 2. College of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China; ;
Jiandong Liu, 3. Beijing Institute of Electronic System Engineering, Beijing 100854, China;

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.

Recommended Citation

Yang, Lu; Yi, Shujuan; Ren, Weijian; and Liu, Jiandong (2020) "Research on Asymptotic Stability for Markovian Jumping Neural Network with Unknown Transition Probabilities," Journal of System Simulation: Vol. 29: Iss. 2, Article 1.
DOI: 10.16182/j.issn1004731x.joss.201702001
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss2/1

First Page

235

Revised Date

2015-07-21

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201702001

Last Page

240

CLC

TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201702001