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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| Published in | Neural processing letters Vol. 54; no. 1; pp. 613 - 635 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.02.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1370-4621 1573-773X |
| DOI | 10.1007/s11063-021-10649-w |
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| Abstract | 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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| AbstractList | 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. |
| Author | Radhika, T. Chandrasekar, A. Zhu, Quanxin |
| Author_xml | – sequence: 1 givenname: A. surname: Chandrasekar fullname: Chandrasekar, A. organization: Department of Mathematics, Sona College of Arts and Science – sequence: 2 givenname: T. surname: Radhika fullname: Radhika, T. email: radhigru@gmail.com organization: Department of Mathematics, Muthayammal Engineering College, School of Electronic Information and Electrical Engineering, Chengdu University – sequence: 3 givenname: Quanxin surname: Zhu fullname: Zhu, Quanxin organization: MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University |
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| Keywords | Cohen–Grossberg BAM neural networks Stochastic systems Input-to-state stability Probabilistic time-varying delay |
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| Title | Further Results on Input-to-State Stability of Stochastic Cohen–Grossberg BAM Neural Networks with Probabilistic Time-Varying Delays |
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