Journal
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS
Volume 10, Issue 4, Pages 2297-2306Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.nonrwa.2008.04.016
Keywords
Neural networks; Global exponential stability; Neuron activation functions; Inverse Lipschitz functions; Topological degree; Matrix inequality
Categories
Funding
- National Natural Science Foundation of China [10571035]
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This paper introduces a new class of functions called inverse Lipschitz functions (IL). By using IL, a novel class of neural networks with inverse Lipschitz neuron activation functions is presented. By the topological degree theory and matrix inequality techniques, the existence and uniqueness of equilibrium point for the neural network are investigated. By constructing appropriate Lyapunov functions, a sufficient condition ensuring global exponential stability of the neural network is given. At last, two numerical examples are given to demonstrate the effectiveness of the results obtained in this paper. (C) 2008 Elsevier Ltd. All rights reserved.
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