Global exponential stability of reaction-diffusion neural networks with both variable time delays and unbounded delay

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Abstract

In the paper, the reaction-diffusion neural network models with both variable time delays and unbounded delay are investigated. These models contain weaker activation functions than partially or globally Lipschitz continuous functions. Without assuming the boundedness, monotonicity and differentiability of the active functions, algebraic criteria ensuring existence, uniqueness and global exponential stability of the equilibrium point are obtained. © Springer-Verlag Berlin Heidelberg 2006.

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APA

Zheng, W., Zhang, J., & Zhang, W. (2006). Global exponential stability of reaction-diffusion neural networks with both variable time delays and unbounded delay. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4113 LNCS-I, pp. 377–384). Springer Verlag. https://doi.org/10.1007/11816157_43

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