Delay-Independent Stability in Bidirectional Associative Memory Networks

K. Gopalsamy; Xue Zhong He

doi:10.1109/72.329700

Delay-Independent Stability in Bidirectional Associative Memory Networks

K. Gopalsamy, Xue Zhong He

Flinders University

Research output: Contribution to journal › Article › peer-review

564 Citations (Scopus)

Abstract

It is shown that if the neuronal gains are small compared with the synaptic connection weights, then a bidirectional associative memory network with axonal signal transmission delays converges to the equilibria associated with exogenous inputs to the network; both discrete and continuously distributed delays are considered; the asymptotic stability is global in the state space of neuronal activations and also is independent of the delays.

Original language	English
Pages (from-to)	998-1002
Number of pages	5
Journal	IEEE Transactions on Neural Networks
Volume	5
Issue number	6
DOIs	https://doi.org/10.1109/72.329700
Publication status	Published - Nov 1994
Externally published	Yes

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title = "Delay-Independent Stability in Bidirectional Associative Memory Networks",

abstract = "It is shown that if the neuronal gains are small compared with the synaptic connection weights, then a bidirectional associative memory network with axonal signal transmission delays converges to the equilibria associated with exogenous inputs to the network; both discrete and continuously distributed delays are considered; the asymptotic stability is global in the state space of neuronal activations and also is independent of the delays.",

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AB - It is shown that if the neuronal gains are small compared with the synaptic connection weights, then a bidirectional associative memory network with axonal signal transmission delays converges to the equilibria associated with exogenous inputs to the network; both discrete and continuously distributed delays are considered; the asymptotic stability is global in the state space of neuronal activations and also is independent of the delays.

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Delay-Independent Stability in Bidirectional Associative Memory Networks

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