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• W2094126907 abstract "In this paper a new learning rule for the coupling weights tuning of Hopfield like chaotic neural networks is developed in such a way that all neurons behave in a synchronous manner, while the desirable structure of the network is preserved during the learning process. The proposed learning rule is based on sufficient synchronization criteria, on the eigenvalues of the weight matrix belonging to the neural network and the idea of Structured Inverse Eigenvalue Problem. Our developed learning rule not only synchronizes all neuron's outputs with each other in a desirable topology, but also enables us to enhance the synchronizability of the networks by choosing the appropriate set of weight matrix eigenvalues. Specifically, this method is evaluated by performing simulations on the scale-free topology." @default.
• W2094126907 created "2016-06-24" @default.
• W2094126907 creator A5071059625 @default.
• W2094126907 creator A5085207296 @default.
• W2094126907 date "2013-06-11" @default.
• W2094126907 modified "2023-09-22" @default.
• W2094126907 title "Synchrony based learning rule of Hopfield like chaotic neural networks with desirable structure" @default.
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• W2094126907 doi "https://doi.org/10.1007/s11571-013-9260-2" @default.
• W2094126907 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3945457" @default.
• W2094126907 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24624234" @default.
• W2094126907 hasPublicationYear "2013" @default.
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