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Finite Time State Estimation of Complex-valued BAM Neutral-type Neural Networks with Time-varying Delays

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Abstract

This paper considers the finite time state estimation problem of complex-valued bidirectional associative memory (BAM) neutral-type neural networks with time-varying delays. By resorting to the Lyapunov function approach, the Wirtinger inequality and the reciprocally convex approach, a delay-dependent criterion in terms of LMIs is established to guarantee the finite-time boundedness of the error-state system for the addressed system. Meanwhile, an effective state estimator is designed to estimate the network states through the available output measurements. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed results.

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Author information

Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, P. R. China

    Runan Guo

  2. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, P. R. China

    Runan Guo

  3. Institute of Complexity Science, Qingdao University, Qingdao, 266071, P. R. China

    Ziye Zhang & Chong Lin

  4. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, P. R. China

    Ziye Zhang

  5. School of Science, Huzhou Teachers College, Huzhou, Zhejiang, 313000, P. R. China

    Yuming Chu & Yongmin Li

Authors
  1. Runan Guo
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  2. Ziye Zhang
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  3. Chong Lin
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  4. Yuming Chu
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  5. Yongmin Li
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Corresponding author

Correspondence to Ziye Zhang.

Additional information

Recommended by Editor Jessie (Ju H.) Park. This work was supported in part by the National Natural Science Foundation of China (61503222, 61573008, 61673227, 61803220, 61773245, 61174076, 61673169), in part by the Research Fund for the Taishan Scholar Project of Shandong Province of China, in part by the Project funded by Chinese Postdoctoral Science Foundation (2016M602166), and in part by the Fund for Postdoctoral Applied Research Projects of Qingdao (2016116).

Runan Guo received the B.S. degree in applied mathematics in 2013 from Yantai University, Yantai, China, and the M.Ec. degree from the College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China, in 2018. She is currently pursuing a Ph.D. degree with the School of Automation, Nanjing University of Science and Technology, Nanjing, China. Her current research interests include neural networks and systems analysis.

Ziye Zhang received the B.Sc. degree in mathematics from Yantai University, Yantai, China, in 2002, the M.Sc. degree in mathematics from Lanzhou University, Lanzhou, China, in 2005, and the Ph.D. degree from the Institute of Complexity Science, Qingdao University, Qingdao, China, in 2015. She was a Post-Doctoral Fellow with Lakehead University, Thunder Bay, ON, Canada, from 2016 to 2017. Since 2005, she has been with the College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China. Her current research interests include systems analysis, fuzzy control, filter design and neural networks.

Chong Lin received his B.Sc. and M.Sc. degrees in applied mathematics from Northeastern University, Shenyang, China, in 1989 and 1992, respectively, and the Ph.D. degree in electrical and electronic Engineering from the Nanyang Technological University, Singapore, in 1999. He was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong, Hong Kong, in 1999. From 2000 to 2006, he was a Research Fellow with the Department of Electrical and Computer Engineering, National University of Singapore. In 2007, he had a short visit as a Visiting Scientist with the Department of Information Systems and Computing, Brunel University, U.K. Since 2006, he has been a Professor with the Institute of Complexity Sicence, Qingdao University, Qingdao, China. His research interests include systems analysis and control, robust control, and fuzzy control.

Yuming Chu was born in June 3, 1966, Huzhou, Zhejiang, China. He received the B.Sc. degree from the Hangzhou Normal University, Hangzhou, China, in 1988, his M.Sc. and Ph.D. degrees from the Hunan University, Changsha, China, in 1991 and 1994, respectively. He worked as an Assistant Professor from 1994 to 1996 and as an Associate Professor from 1997 to 2002 at the Department of Mathematics, Hunan Normal University, Changsha, China. Since 2002, he has been a Professor and Dean in the Department of Mathematics at Huzhou Teachers College, Huzhou, China. Dr. Chu’s current research interests include robust filtering and control, special function, quasiconformal mapping and complex dynamic systems.

Yongmin Li received his B.S. in Mathematics from Shanxi Normal University, an M.S in Operational Research and Cybernetics from Guizhou University and a Ph.D. in Control Theory and Control Engineering from Nanjing University of Science and Technology, in 1992, 2002, and 2008, respectively. He is currently a professor of School of Science, Huzhou Teachers College, Huzhou, China. His current research interest includes robust control, anti-windup compensator design and time-delay systems.

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Guo, R., Zhang, Z., Lin, C. et al. Finite Time State Estimation of Complex-valued BAM Neutral-type Neural Networks with Time-varying Delays. Int. J. Control Autom. Syst. 17, 801–809 (2019). https://doi.org/10.1007/s12555-018-0542-7

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