Gain-Scheduled Finite-Time Synchronization for Reaction-Diffusion Memristive Neural Networks Subject to Inconsistent Markov Chains

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 7; pp. 2952 - 2964
• Main Authors: Song, Xiaona, Man, Jingtao, Song, Shuai, Ahn, Choon Ki
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3009081

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.