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

Author

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  • Liu, Yicheng
  • Liao, Xiaofeng

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.

Suggested Citation

  • Liu, Yicheng & Liao, Xiaofeng, 2019. "FLS-based finite-time synchronization of delayed memristive neural networks with interval parameters and nonlinear couplings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    • Handle: RePEc:eee:phsmap:v:533:y:2019:i:c:s0378437119311136
    DOI: 10.1016/j.physa.2019.121890
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    Cited by:

    1. Zhou, Wenjia & Hu, Yuanfa & Liu, Xiaoyang & Cao, Jinde, 2022. "Finite-time adaptive synchronization of coupled uncertain neural networks via intermittent control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    2. Mao, Kun & Liu, Xiaoyang & Cao, Jinde & Hu, Yuanfa, 2022. "Finite-time bipartite synchronization of coupled neural networks with uncertain parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).

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