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Exponential Stabilization of Memristor-based Recurrent Neural Networks with Disturbance and Mixed Time Delays via Periodically Intermittent Control

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  • Intelligent Control and Applications
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Abstract

A periodically intermittent control is considered for the memristor-based recurrent neural networks with disturbance and mixed time delays. The purpose of this study is to design the controller with less constraints, which is convenient for successful applications of memristor-based recurrent neural networks in complex environment. First, the estimate of the disturbance and mixed time delay are given by the assumptions. Then, by the method of Lyapunov-Krasovski functional, two new criteria ensuring globally exponential stabilization of the neural network are obtained under the controller, respectively. The proposed theoretical results indicate that the control width and control period are not constrained except that the control width has an upper bound. Furthermore, the design algorithm of controller is described. Finally, two examples are performed through four neural networks to illustrate the effectiveness of the proposed results.

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Authors and Affiliations

  1. School of Science and School of Electronics and Information Engineering, Ningbo University of Technology, Ningbo, 315211, China

    Jianmin Wang & Fengqiu Liu

  2. Department of Mathematics, Harbin Institute of Technology, Weihai, 264209, China

    Sitian Qin

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  1. Jianmin Wang
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  2. Fengqiu Liu
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  3. Sitian Qin
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Correspondence to Jianmin Wang.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was jointly supported by the National Natural Science Foundation of China (11201100), and the Research Funds of NingBo University of Technology (2090011540011).

Jianmin Wang received his Ph.D. degree in instruments science and technology from the Harbin Institute of Technology, Harbin, China, in 2011. He is currently an Associate Professor with the Ningbo University of Technology, Ningbo, China. His main scientific interests are in the field of neural network, circuit control and fault diagnosis.

Fengqiu Liu received her Ph.D. degree in mathematics from the Harbin Institute of Technology, Harbin, China, in 2011. She is currently a Professor with the Ningbo University of Technology, Ningbo, China. Her main scientific interests are in the field of neural network and machine learning with emphasis on kernel methods.

Sitian Qin was born in Shandong, China, in 1981. He received his Ph.D. degree in mathematics from the Harbin Institute of Technology, Harbin, China, in 2010. He is currently a Professor with the Harbin Institute of Technology, Weihai, China, where he was an Assistant Professor from 2010 to 2013 and an Associate Professor from 2013 to 2018. His current research interests include neural network theory and optimization.

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Wang, J., Liu, F. & Qin, S. Exponential Stabilization of Memristor-based Recurrent Neural Networks with Disturbance and Mixed Time Delays via Periodically Intermittent Control. Int. J. Control Autom. Syst. 19, 2284–2296 (2021). https://doi.org/10.1007/s12555-020-0083-8

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  • DOI: https://doi.org/10.1007/s12555-020-0083-8

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