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Finite-Time Robust Stabilization for Stochastic Neural Networks

Author

Listed:
  • Weixiong Jin
  • Xiaoyang Liu
  • Xiangjun Zhao
  • Nan Jiang
  • Zhengxin Wang

Abstract

This paper is concerned with the finite-time stabilization for a class of stochastic neural networks (SNNs) with noise perturbations. The purpose of the addressed problem is to design a nonlinear stabilizator which can stabilize the states of neural networks in finite time. Compared with the previous references, a continuous stabilizator is designed to realize such stabilization objective. Based on the recent finite-time stability theorem of stochastic nonlinear systems, sufficient conditions are established for ensuring the finite-time stability of the dynamics of SNNs in probability. Then, the gain parameters of the finite-time controller could be obtained by solving a linear matrix inequality and the robust finite-time stabilization could also be guaranteed for SNNs with uncertain parameters. Finally, two numerical examples are given to illustrate the effectiveness of the proposed design method.

Suggested Citation

  • Weixiong Jin & Xiaoyang Liu & Xiangjun Zhao & Nan Jiang & Zhengxin Wang, 2012. "Finite-Time Robust Stabilization for Stochastic Neural Networks," Abstract and Applied Analysis, Hindawi, vol. 2012, pages 1-15, October.
    • Handle: RePEc:hin:jnlaaa:231349
    DOI: 10.1155/2012/231349
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