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Exponential Synchronization of Stochastic Memristive Recurrent Neural Networks Under Alternate State Feedback Control

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  • Control Theory and Applications
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Abstract

This paper solves the exponential synchronization problem of two memristive recurrent neural networks with both stochastic disturbance and time-varying delays via periodically alternate state feedback control. First, a periodically alternate state feedback control rule is designed. Then, on the basis of the Lyapunov stability theory, some novel sufficient conditions guaranteeing exponential synchronization of drive-response stochastic memristive recurrent neural networks via periodically alternate state feedback control are derived. In contrast to some previous works about synchronization of memristive recurrent neural networks, the obtained results in this paper are not difficult to be validated, and complement, extend and generalize the earlier papers. Lastly, an illustrative example is provided to indicate the effectiveness and applicability of the obtained theoretical results.

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Authors and Affiliations

  1. School of Electrical Engineering, Yancheng Institute of Technology, Yancheng, 224051, China

    Xiaofan Li & Huiyuan Li

  2. College of Information Science and Technology, Donghua University, Shanghai, 201620, China

    Jian-an Fang

Authors
  1. Xiaofan Li
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  2. Jian-an Fang
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  3. Huiyuan Li
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Corresponding author

Correspondence to Xiaofan Li.

Additional information

Recommended by Associate Editor Jun Cheng under the direction of Editor Jessie (Ju H.) Park. The work was supported in part by the Natural Science Foundation of China under Grants 61603325, in part by the Innovation Program of Shanghai Municipal Education Commission under Grants 13ZZ050.

Xiaofan Li received the B.S. degree in electrical engineering and automation from Yancheng Institute of Technology, Yancheng, China, in 2004, and the M.Sc. degree in detection technology and automation equipment from Anhui Polytechnic University, Wuhu, China, in 2007. He is currently pursuing his Ph.D. degree in control science and engineering from Donghua University, Shanghai, China. He is currently an Associate Professor of the School of Electrical Engineering, Yancheng Institute of Technology, Yancheng, China. His current research interests include synchronization/stabilization, neural networks, and memristors.

Jian-an Fang has been a Professor with Donghua University since 2001. He joined the College of Information Science and Technology, Donghua University. In 1998 and 1998, he was a Visiting Scholar with the University of Michigan, Ann Arbor, MI, USA. From 1998 and 1999, he was a Visiting Scholar with the University of Maryland, College Park, MD, USA. From 2005 to 2005, he was the Senior Visiting Scholar with the University of Southern California, Los Angeles, CA, USA. Prof. Fang was a Council Member of the Shanghai Automation Association and the Shanghai Microcomputer Applications in 2005 and 2006.

Huiyuan Li received the B.S. degree in electrical engineering and automation from Yancheng Institute of Technology, Yancheng, China, in 2012, and the M.Sc. degree in detection technology and automation equipment from Anhui Polytechnic University, Wuhu, China, in 2015. She is a teacher of the School of Electrical Engineering, Yancheng Institute of Technology, Yancheng, China. Her current research interests include synchronization/stabilization, neural networks, and multi-agent systems.

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Li, X., Fang, Ja. & Li, H. Exponential Synchronization of Stochastic Memristive Recurrent Neural Networks Under Alternate State Feedback Control. Int. J. Control Autom. Syst. 16, 2859–2869 (2018). https://doi.org/10.1007/s12555-018-0225-4

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  • DOI: https://doi.org/10.1007/s12555-018-0225-4

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