Abstract
The stability of neural networks is fundamental for successful applications of the networks. In this paper, the exponential stability of Cohen-Grossberg Neural Networks is investigated. To avoid the difficulty of the construction of a proper Lyapunov function, a new concept named generalized relative nonlinear measure is introduced, and thus a novel approach to stability analysis of neural networks is developed. With this new approach, sufficient conditions for the existence, uniqueness of the equilibrium and the exponential stability of the Cohen-Grossberg neural networks are presented. Meanwhile, the exponential convergence rate of the networks to stable equilibrium point is estimated, and the attraction region of local stable equilibrium point is also characterized.
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© 2004 Springer-Verlag Berlin Heidelberg
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Wan, A., Mao, W., Zhao, C. (2004). A Novel Approach to Exponential Stability Analysis of Cohen-Grossberg Neural Networks. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_16
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DOI: https://doi.org/10.1007/978-3-540-28647-9_16
Publisher Name: Springer, Berlin, Heidelberg
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