Abstract
In this paper the issue of structure based learning of Hopfield like chaotic neural networks is investigated in such a way that all neurons behave in a synchronous manner. By utilizing the idea of structured inverse eigenvalue problem and the sufficient conditions on the coupling weights of a network which guarantee the synchronization of all neuron’s outputs, we propose a learning method for tuning the coupling weights of a network where not only synchronize all neuron’s outputs with each other but also brings about any desirable topology for the structure of the network. Specifically, this method is evaluated by performing simulations on the scale-free topology.
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Mahdavi, N., Kurths, J. (2012). Synchronization of Hopfield Like Chaotic Neural Networks with Structure Based Learning. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_15
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DOI: https://doi.org/10.1007/978-3-642-34481-7_15
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