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Showing papers on "Asynchronous learning published in 1991"


01 Jan 1991
TL;DR: In this paper, conditions of configuring feed-forward neural networks without local minima are analyzed for both synchronous and asynchronous learning rules, and a learning algorithm that integrates a synchronous-asynchronous learning rule with a dynamic configuration rule is presented to train feed forward neural networks.
Abstract: Conditions of configuring feedforward neural networks without local minima are analyzed for both synchronous and asynchronous learning rules. Based on the analysis, a learning algorithm that integrates a synchronous-asynchronous learning rule with a dynamic configuration rule is presented to train feedforward neural networks. The theoretic analysis and numerical simulation reveal that the proposed learning algorithm substantially reduces the likelihood of local minimum solutions in supervised learning.