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The Optimization of Control Parameters: Finite-time and Fixed-time Synchronization of Inertial Memristive Neural Networks with Proportional Delays and Switching Jumps Mismatch

Qi Chang, Yongqing Yang*, Li Li*, and Fei Wang
International Journal of Control, Automation, and Systems, vol. 19, no. 7, pp.2491-2499, 2021

Abstract : This thesis’s object is inertial memristive neural networks (IMNNs) with proportional delays and switching jumps mismatch. Different from the traditional bounded delay, the proportional delay will be infinite as t → ∞. The finite-time synchronization (FN-TS) and fixed-time synchronization (FX-TS) can be realized with the devised controllers for the drive-response systems (D-RSs). Along with the Lyapunov function and some inequalities, the synchronization criteria of D-RSs are given. This paper presents an optimization model with minimum control energy and dynamic error as objective functions, aiming to obtain more accurate and optimized controller parameters. An intelligent algorithm: particle swarm optimization with stochastic inertia weight (SIWPSO) algorithm is introduced to solve the optimization model. Meanwhile, an integrated algorithm for selecting optimal control parameters is presented as well. In this method, the optimal control parameters and the setting time of synchronization can be obtained directly. At last, some simulations are presented to verify the theorems and the optimization model.

Keyword : Finite-time & fixed-time, inertial memristive neural networks, parameter optimization via SIWPSO, proportional delays, switching jumps mismatch.

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