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Global Exponential Stability of Recurrent Neural Networks with Time-Varying Delay

  • Yi Shen
  • Meiqin Liu
  • Xiaodong Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

A new theoretical result on the global exponential stability of recurrent neural networks with time-varying delay is presented. It should be noted that the activation functions of recurrent neural network do not require to be bounded. The presented criterion, which has the attractive feature of possessing the structure of linear matrix inequality, is a generalization and improvement over some previous criteria.

Keywords

Linear Matrix Inequality Global Stability Recurrent Neural Network Piecewise Linear Function Librium Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yi Shen
    • 1
  • Meiqin Liu
    • 2
  • Xiaodong Xu
    • 3
  1. 1.Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.College of Electrical EngineeringZhejiang UniversityHangzhouChina
  3. 3.College of Public AdministrationHuazhong University of Science and TechnologyWuhanChina

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