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Asymptotical synchronization for delayed stochastic neural networks with uncertainty via adaptive control

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Abstract

In this paper, the problem of the adaptive synchronization control is considered for neural networks with uncertainty and stochastic noise. Via utilizing stochastic analysis method and linear matrix inequality (LMI) approach, several sufficient conditions to ensure the adaptive synchronization for neural networks are derived. By the adaptive feedback methods, some suitable parameters update laws are found. Finally, a simulation result is provided to substantiate the effectiveness of the proposed approach.

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Author information

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Dongbing Tong & Li** Zhang

  2. Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, China

    Dongbing Tong

  3. College of Information Sciences and Technology, Donghua University, Shanghai, 200051, China

    Wuneng Zhou & Jun Zhou

  4. School of Finance, Nan**g Audit University, Jiangsu, 211815, China

    Yuhua Xu

Authors
  1. Dongbing Tong
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  2. Li** Zhang
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  3. Wuneng Zhou
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  4. Jun Zhou
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  5. Yuhua Xu
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Corresponding authors

Correspondence to Dongbing Tong, Li** Zhang or Wuneng Zhou.

Additional information

Recommended by Editor Ju Hyun Park. This work was supported by the National Natural Science Foundation of China (11501367), the Chinese Postdoctoral Science Foundation (2015M581528), the Natural Science Foundation of Shanghai (15ZR1419000, 15ZR1401800), the Young Teacher Training Scheme of Shanghai Universities (ZZGCD15004, ZZLX15031), Doctoral Starting Foundation of Shanghai University of Engineering Science (**aoqi 2015-21), the Youth Fund Project of the Humanities and Social Science Research for the Ministry of Education of China (14YJCZH173), the Science and Technology Research Key Program for the Education Department of Hubei Province of China (D20155001, D20156001), and the Science and Technology Research Youth Project for the Education Department of Hubei Province of China (Q20145001).

Dongbing Tong received his Ph.D. degree in Control Theory and Control Engineering from Donghua University, Shanghai, China, in 2014. He is currently a Lecturer at Shanghai University of Engineering Science, Shanghai, China. His current research interests include complex networks, and model reduction.

Li** Zhang received his M.S. degree in Control Theory and Control Engineering from Donghua University, Shanghai, China, in 2010. She is currently a Professor at Shanghai University of Engineering Science, Shanghai, China. Her current research interests include communication system, and detection system.

Wuneng Zhou received a first class B.S. degree from Huazhong Normal University in 1982. He obtained his Ph.D. degree from Zhejiang University in 2005. Now he is a professor in Donghua University, Shanghai. His current research interests include the stability, the synchronization, control of neural networks and complex networks.

Jun Zhou received the M.S. degree in computer application technology from Yunnan University, Yunnan, China, in 2010. Now he is under a Ph.D. candidate in control science and engineering form Donghua University, Shanghai and a teacher in Southwest Forestry University, Yunnan, China. His current research interests include the stability, the synchronization and control for neural networks.

Yuhua Xu received his Ph.D. degree in Control Theory and Control Engineering from Donghua University, P. R. China in 2011. Currently he is a Professor at Nan**g Audit University. His research interests include the network control, nonlinear finance systems, dynamics and control.

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Tong, D., Zhang, L., Zhou, W. et al. Asymptotical synchronization for delayed stochastic neural networks with uncertainty via adaptive control. Int. J. Control Autom. Syst. 14, 706–712 (2016). https://doi.org/10.1007/s12555-015-0077-0

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  • DOI: https://doi.org/10.1007/s12555-015-0077-0

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