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Global Exponential Stability in Lagrange Sense of Continuous-Time Recurrent Neural Networks

  • Xiaoxin Liao
  • Zhigang Zeng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

In this paper, global exponential stability in Lagrange sense is further studied for continuous recurrent neural network with three different activation functions. According to the parameters of the system itself, detailed estimation of global exponential attractive set, and positive invariant set is presented without any hypothesis on existence. It is also verified that outside the global exponential attracting set; i.e., within the global attraction domain, there is no equilibrium point, periodic solution, almost periodic solution, and chaos attractor of the neural network. These theoretical analysis narrowed the search field of optimization computation and associative memories, provided convenience for application.

Keywords

Neural Network Periodic Solution Equilibrium Point Global Stability Recurrent Neural Network 
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

  • Xiaoxin Liao
    • 1
  • Zhigang Zeng
    • 2
  1. 1.Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of AutomationWuhan University of TechnologyWuhanChina

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