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Global stability of stochastic, discrete-time neural networks

Joy, MP (2005) Global stability of stochastic, discrete-time neural networks Proceedings of the 9th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2005. pp. 130-134.

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The stability analysis of neural networks is important in the applications and has been studied by many authors. However, only recently has the stability of stochastic models of neural networks been investigated. In this paper we analyse the global asymptotic stability of a class of neural networks described by a stochastic difference equation, in fact, a Markov chain with state space Rm. If Xn is the state of the neural network at time n, we prove that under certain conditions, Xn → 0, n→∞ and are able to bound sample Lyapunov exponents -it turns out that our model is exponentially stable under these conditions. Our results assume neither the symmetry of the interconnection weights, neither do we assume differentiability or monotonicity of the activation functions.

Item Type: Article
Divisions : Surrey research (other units)
Authors :
Date : 1 December 2005
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 10:29
Last Modified : 24 Jan 2020 19:26

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