Gain-Scheduled Finite-Time Synchronization for Reaction-Diffusion Memristive Neural Networks Subject to Inconsistent Markov Chains
An innovative class of drive-response systems that are composed of Markovian reaction-diffusion memristive neural networks, where the drive and response systems follow inconsistent Markov chains, is proposed in this article. For this kind of nonlinear parameter-varying systems, a suitable gain-sched...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 32; no. 7; pp. 2952 - 2964 |
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01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | An innovative class of drive-response systems that are composed of Markovian reaction-diffusion memristive neural networks, where the drive and response systems follow inconsistent Markov chains, is proposed in this article. For this kind of nonlinear parameter-varying systems, a suitable gain-scheduled controller that involves a mode and memristor-dependent item is designed, so that the error system is bounded within a finite-time interval. Moreover, by constructing a novel Lyapunov-Krasovskii functional and employing the canonical Bessel-Legendre inequality and free-weighting matrix method, the conservatism of the finite-time synchronization criterion can be greatly reduced. Finally, two numerical examples are provided to illustrate the feasibility and practicability of the obtained results. |
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| AbstractList | An innovative class of drive-response systems that are composed of Markovian reaction–diffusion memristive neural networks, where the drive and response systems follow inconsistent Markov chains, is proposed in this article. For this kind of nonlinear parameter-varying systems, a suitable gain-scheduled controller that involves a mode and memristor-dependent item is designed, so that the error system is bounded within a finite-time interval. Moreover, by constructing a novel Lyapunov–Krasovskii functional and employing the canonical Bessel–Legendre inequality and free-weighting matrix method, the conservatism of the finite-time synchronization criterion can be greatly reduced. Finally, two numerical examples are provided to illustrate the feasibility and practicability of the obtained results. An innovative class of drive-response systems that are composed of Markovian reaction-diffusion memristive neural networks, where the drive and response systems follow inconsistent Markov chains, is proposed in this article. For this kind of nonlinear parameter-varying systems, a suitable gain-scheduled controller that involves a mode and memristor-dependent item is designed, so that the error system is bounded within a finite-time interval. Moreover, by constructing a novel Lyapunov-Krasovskii functional and employing the canonical Bessel-Legendre inequality and free-weighting matrix method, the conservatism of the finite-time synchronization criterion can be greatly reduced. Finally, two numerical examples are provided to illustrate the feasibility and practicability of the obtained results.An innovative class of drive-response systems that are composed of Markovian reaction-diffusion memristive neural networks, where the drive and response systems follow inconsistent Markov chains, is proposed in this article. For this kind of nonlinear parameter-varying systems, a suitable gain-scheduled controller that involves a mode and memristor-dependent item is designed, so that the error system is bounded within a finite-time interval. Moreover, by constructing a novel Lyapunov-Krasovskii functional and employing the canonical Bessel-Legendre inequality and free-weighting matrix method, the conservatism of the finite-time synchronization criterion can be greatly reduced. Finally, two numerical examples are provided to illustrate the feasibility and practicability of the obtained results. |
| Author | Song, Shuai Ahn, Choon Ki Man, Jingtao Song, Xiaona |
| Author_xml | – sequence: 1 givenname: Xiaona orcidid: 0000-0001-8476-5112 surname: Song fullname: Song, Xiaona email: xiaona_97@163.com organization: School of Information Engineering, Henan University of Science and Technology, Luoyang, China – sequence: 2 givenname: Jingtao orcidid: 0000-0003-2184-503X surname: Man fullname: Man, Jingtao email: mjt546@163.com organization: School of Information Engineering, Henan University of Science and Technology, Luoyang, China – sequence: 3 givenname: Shuai orcidid: 0000-0002-4780-0967 surname: Song fullname: Song, Shuai email: songshuai_1010@163.com organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China – sequence: 4 givenname: Choon Ki orcidid: 0000-0003-0993-9658 surname: Ahn fullname: Ahn, Choon Ki email: hironaka@korea.ac.kr organization: School of Electrical Engineering, Korea University, Seoul, South Korea |
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| Snippet | An innovative class of drive-response systems that are composed of Markovian reaction-diffusion memristive neural networks, where the drive and response... An innovative class of drive-response systems that are composed of Markovian reaction–diffusion memristive neural networks, where the drive and response... |
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| SubjectTerms | Artificial neural networks Canonical Bessel–Legendre (B–L) inequality Diffusion finite-time synchronization gain-scheduled controller inconsistent Markov chains Learning systems Markov analysis Markov chains Markov processes Markovian reaction–diffusion memristive neural networks (MNNs) Memristors Neural networks Nonhomogeneous media Nonlinear systems Synchronization Time synchronization |
| Title | Gain-Scheduled Finite-Time Synchronization for Reaction-Diffusion Memristive Neural Networks Subject to Inconsistent Markov Chains |
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