Extended Robust Exponential Stability of Fuzzy Switched Memristive Inertial Neural Networks With Time-Varying Delays on Mode-Dependent Destabilizing Impulsive Control Protocol

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 1; pp. 308 - 321
• Main Authors: Yu, Yongbin, Wang, Xiangxiang, Zhong, Shouming, Yang, Nijing, Tashi, Nyima
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-01-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Computer Simulation; Control stability; Control theory; Delays; Dwell time; Extended robust exponential stability; Feedback; Feedback control; fuzzy; Fuzzy Logic; impulsive effects; memristive inertial neural networks; Memristors; mode dependent; Models, Theoretical; Neural networks; Neural Networks, Computer; Robustness (mathematics); Stability analysis; switched; Switches; Time varying control
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2020.2978542

This article investigates the problem of robust exponential stability of fuzzy switched memristive inertial neural networks (FSMINNs) with time-varying delays on mode-dependent destabilizing impulsive control protocol. The memristive model presented here is treated as a switched system rather than employing the theory of differential inclusion and set-value map. To optimize the robust exponentially stable process and reduce the cost of time, hybrid mode-dependent destabilizing impulsive and adaptive feedback controllers are simultaneously applied to stabilize FSMINNs. In the new model, the multiple impulsive effects exist between two switched modes, and the multiple switched effects may also occur between two impulsive instants. Based on switched analysis techniques, the Takagi-Sugeno (T-S) fuzzy method, and the average dwell time, extended robust exponential stability conditions are derived. Finally, simulation is provided to illustrate the effectiveness of the results.