Exponential stability of hybrid stochastic neural networks with mixed time delays and nonlinearity |
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| Authors: | Wuneng Hongqian Chunmei |
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| Affiliation: | aCollege of Information Science and Technology, Donghua University, Shanghai 201620, China;bEngineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China;cSchool of Management and Economics, Shandong Normal University, Shandong 250014, China;dSchool of Electronic Information and Control Engineering, Shandong Institute of Light Industry, Shandong 250353, China |
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| Abstract: | This paper is concerned with the problem of robust exponential stability for a class of hybrid stochastic neural networks with mixed time-delays and Markovian jumping parameters. In this paper, free-weighting matrices are employed to express the relationship between the terms in the Leibniz–Newton formula. Based on the relationship, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions for the mixed time-delays neural networks with Markovian jumping parameters. Finally, two simulation examples are provided to demonstrate the effectiveness of the results developed. |
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| Keywords: | Neural networks Uncertain systems Stochastic systems Mixed time-delays Exponential stability |
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