Abstract
The exponentially decaying tabu search, which exhibits high performance in solving quadratic assignment problems (QAPs), has been implemented on a neural network with chaotic neurodynamics. To exploit the inherent parallel processing capability of analog hardware systems, a synchronous updating scheme, in which all neurons in the network are updated simultaneously, has also been proposed. However, several neurons may fire simultaneously with the synchronous updating. As a result, we cannot determine only one candidate for the 2-opt exchange from among the many fired neurons. To solve this problem, several neuron selection methods, which select a specific neuron from among the fired neurons, have been devised. These neuron selection methods improved the performance of the synchronous updating scheme; however, the dynamics of the chaotic neural network driven by these heuristic algorithms cannot be intuitively understood. In this paper, we analyze the dynamics of a chaotic neural network driven by the neuron selection methods by considering the spatial and temporal mutual information.
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Kawamura, T., Horio, Y., Hasegawa, M. (2010). Mutual Information Analyses of Chaotic Neurodynamics Driven by Neuron Selection Methods in Synchronous Exponential Chaotic Tabu Search for Quadratic Assignment Problems. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_7
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DOI: https://doi.org/10.1007/978-3-642-17537-4_7
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