Abstract
In this paper, we consider a class of time-delay artificial neural networks and obtain practical criteria to test asymptotic stability of the equilibrium of the time-delay artificial neural networks, with or without perturbations. These criteria require verification of the definiteness of a certain matrix, or verification of a certain inequality. Furthermore, we discuss the exponential stability and estimate the exponential convergence rate for time-delay artificial neural networks. The applicability of our results is demonstrated by means of two specific examples.
The research is supported by the National Natural Science Foundation of China under Grant 69934030.
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Wang, RL., Liu, YQ. (2005). Delay-Dependent Stability Analysis for a Class of Delayed Neural Networks. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_109
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DOI: https://doi.org/10.1007/11538059_109
Publisher Name: Springer, Berlin, Heidelberg
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