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New Results on the Robust Stability of Cohen–Grossberg Neural Networks with Delays

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Abstract

In this letter, a generalized type of Cohen–Grossberg neural networks with time delays are discussed and their global robust stability of the equilibrium point is investigated. By introducing a set of Lyapunov functionals, several new sufficient conditions guaranteeing the global robust convergence are derived. The results show that the amplification function a i (x) is harmless to the robust stability of Cohen–Grossberg neural networks. Two examples are given to demonstrate the applicability of the proposed results.

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Authors and Affiliations

  1. Department of Mathematics, Purdue University, West Lafayette, IN, 47907, USA

    Libin Rong

  2. Laboratory of Nonlinear Science, Institute of Mathematics, Fudan University, Shanghai, 200433, PR China

    Tianping Chen

Authors
  1. Libin Rong
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  2. Tianping Chen
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Correspondence to Libin Rong.

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Rong, L., Chen, T. New Results on the Robust Stability of Cohen–Grossberg Neural Networks with Delays. Neural Process Lett 24, 193–202 (2006). https://doi.org/10.1007/s11063-006-9010-0

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  • DOI: https://doi.org/10.1007/s11063-006-9010-0

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