Further Results on Input-to-State Stability of Stochastic Cohen–Grossberg BAM Neural Networks with Probabilistic Time-Varying Delays

In this article, the problem of stochastic Cohen–Grossberg Bidirectional Associative Memory (CGBAM) neural networks with probabilistic time-varying delay is analyzed by input-to-state stability theory. The stochastic variable with Bernoulli distribution gives the information of probabilistic time-va...

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Bibliographic Details
Published inNeural processing letters Vol. 54; no. 1; pp. 613 - 635
Main Authors Chandrasekar, A., Radhika, T., Zhu, Quanxin
Format Journal Article
LanguageEnglish
Published New York Springer US 01.02.2022
Springer Nature B.V
Subjects
Artificial Intelligence
Associative memory
Complex Systems
Computational Intelligence
Computer Science
Delay
Inequality
Neural networks
Probability theory
Stability analysis
Cohen–Grossberg BAM neural networks
Stochastic systems
Input-to-state stability
Probabilistic time-varying delay
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Summary:In this article, the problem of stochastic Cohen–Grossberg Bidirectional Associative Memory (CGBAM) neural networks with probabilistic time-varying delay is analyzed by input-to-state stability theory. The stochastic variable with Bernoulli distribution gives the information of probabilistic time-varying delay and it is transformed into one with deterministic time-varying delay in the stochastic manner. Further, by constructing a novel Lyapunov–Krasovskii functional and utilizing Ito’s and Dynkin’s formula with stochastic analysis theory, the sufficient criterion is derived for the input-to-state stability of stochastic CGBAM neural networks. Finally, numerical examples are provided to examine the merits of the given method.
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ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-021-10649-w