Mean square exponential stabilization analysis of stochastic neural networks with saturated impulsive input.

Journal: Neural networks : the official journal of the International Neural Network Society
PMID: 37977089

Abstract

The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid and the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results.

Authors

  • Hao Deng
    Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
  • Chuandong Li
    College of Electronic and Information Engineering, Southwest University, Chongqing 400044, PR China. Electronic address: licd@cqu.edu.cn.
  • Fei Chang
    Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China. Electronic address: changfei0602@swu.edu.cn.
  • Yinuo Wang
    Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China. Electronic address: wangynswu@163.com.

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