FLS-based finite-time synchronization of delayed memristive neural networks with interval parameters and nonlinear couplings

Yicheng Liu and Xiaofeng Liao

Physica A: Statistical Mechanics and its Applications, 2019, vol. 533, issue C

Abstract: This paper is concerned with the problem of the finite-time drive–response synchronization of a general class of delayed memristive neural networks (DMNN) with interval parameters, fuzzy logical system-based model and nonlinear couplings. An Takagi–Sugeno (TS) fuzzy logic system (FLS) is introduced with memristor-based neuromorphic network which exhibits a tremendous advantage in terms of building artificial brain, and due to the unexpected parameter mismatch in DMNN when different initial conditions are selected, the Filippov-framework and the interval matrix method are considered to reduce the conservativeness of the finite-time synchronization criteria. By utilizing the novel discontinuous controller with the discontinuous state feedback and the adaptive term in the Lyapunov functional framework, some concise conditions are acquired to guarantee the finite-time drive–response synchronization of DMNN. In addition, it is shown that a unified condition for the finite-time synchronization of fuzzy-based DMNN with nonlinear couplings is derived. Finally, several simulated examples are also presented to demonstrate the correctness of the theoretical results.

Keywords: Delayed memristive neural networks(DMNN); Fuzzy logical system(FLS); Interval parameters; Nonlinear couplings; Discontinuous controller (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:533:y:2019:i:c:s0378437119311136

DOI: 10.1016/j.physa.2019.121890

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