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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

  • Control Theory and Applications
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International Journal of Control, Automation and Systems Aims and scope Submit manuscript

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.

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Author information

Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi, 214122, P. R. China

    Qi Chang

  2. School of Science, Jiangnan University, Wuxi, 214122, China

    Yongqing Yang & Li Li

  3. School of Mathematical Sciences, Qufu Normal University, Qufu, 273165, China

    Fei Wang

Authors
  1. Qi Chang
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  2. Yongqing Yang
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  3. Li Li
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  4. Fei Wang
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Corresponding authors

Correspondence to Yongqing Yang or Li Li.

Additional information

This work was jointly supported by the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20170171.

Qi Chang received her B.S. degree in information and computing science from Jiangnan University in 2017. Now she is a Ph.D. candidate in the School of IoT Engineering, Jiangnan University. Her research interests include memristive neural networks, parameter optimization, and finite-time synchronization.

Yongqing Yang received his B.S. degree from Anhui Normal University, an M.S. degree from Anhui University of Science and Technology, and a Ph.D. degree from Southeast University, in 1985, 1992, and 2007, respectively. His research interests include nonlinear systems, neural networks, and optimization.

Li Li received her B.S. degree in mathematics and an M.S. degree in applied mathematics from Anhui Normal University, a Ph.D. degree in control science and engineering from Jiangnan University, in 2001, 2007, and 2016, respectively. Her research interests include neural networks and computational intelligence.

Fei Wang received his B.S. and Ph.D. degrees from Jiangnan University, in 2013 and 2018, respectively. His research interests include dynamic and control of fractional order systems and nonlinear systems.

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Chang, Q., Yang, Y., Li, L. et al. The Optimization of Control Parameters: Finite-time and Fixed-time Synchronization of Inertial Memristive Neural Networks with Proportional Delays and Switching Jumps Mismatch. Int. J. Control Autom. Syst. 19, 2491–2499 (2021). https://doi.org/10.1007/s12555-020-0425-6

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  • DOI: https://doi.org/10.1007/s12555-020-0425-6

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