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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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2021-07, Vol.32 (7), p.2952-2964
Main Authors: Song, Xiaona, Man, Jingtao, Song, Shuai, Ahn, Choon Ki
Format: Article
Language:English
Subjects:
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
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Summary: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.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2020.3009081