Extended dissipative state estimation for memristive neural networks with time-varying delay.

Journal: ISA transactions
Published Date: Jun 2, 2016

Abstract

This paper investigates the problem of extended dissipative state estimation for memristor-based neural networks (MNNs) with time-varying delay. Based on both nonsmooth analysis and the construction of a new Lyapunov-Krasovskii functional, the extended dissipative state estimation criteria are obtained by mainly applying differential inclusions, set-valued maps and many new integral inequalities. The extended dissipative state estimation can be adopted to deal with l2-l∞ state estimation, H∞ state estimation, passive state estimation and dissipative state estimation by valuing the corresponding weighting matrices. Finally, two numerical examples are given to show the effectiveness and less conservatism of the proposed criteria.

Authors

  • Jianying Xiao
    School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, PR China; School of Sciences, Southwest Petroleum University, Chengdu 610050, PR China. Electronic address: shawion1980@yahoo.com.
  • Yongtao Li
    College of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610050, PR China.
  • Shouming Zhong
    School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China; Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
  • Fang Xu
    CAS Key Laboratory of Urban Pollutant Conversion, Department of Chemistry, University of Science & Technology of China, Hefei 230026, China; School of Medical Engineering, Hefei University of Technology, Hefei 230009, China.

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