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Exponential stability of additive neural networks

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2 Author(s)
Hua Yang ; Dept. of Comput. Sci. & Comput. Eng., La Trobe Univ., Bundoora, Vic., Australia ; Dillon, T.S.

Exponential and stochastic stabilities of additive neural networks are analyzed. The results are especially suitable for asymmetric neural networks. A constraint on the connection matrix has been founded under which the neural network has a unique and exponentially stable equilibrium. Given any real matrix, this constraint can be satisfied if the gain coefficients and resistances in the neural net circuit are suitably adjusted. A one-to-one and smooth map between input currents and the equilibria of the neural network can be set up. The uniqueness results can be applied to analyze the master/slave net. For the neural network disturbed by some noise, the stochastic stability of the network is also discussed

Published in:

Neural Networks, 1992. IJCNN., International Joint Conference on  (Volume:4 )

Date of Conference:

7-11 Jun 1992