Abstract
High order network has a higher store capacity and a faster convergence speed compared with the first order network. To improve the convergence speed of the energy function, in this paper a new kind of high order discrete neural network with self-feedback is proposed to solve crossbar switch problem. The construction method of the high order energy function for this problem is presented and the neural computing method is given. We also discuss the strategies for the network to escape from local minima. Compared with the first order Hopfield network, experimental results show the high order network with self-feedback has a quick convergence speed, its performance is better than the first order Hopfield network.
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Ding, Y., Dong, L., Zhao, B., Lu, Z. (2011). High Order Hopfield Network with Self-feedback to Solve Crossbar Switch Problem. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_35
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DOI: https://doi.org/10.1007/978-3-642-24965-5_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24964-8
Online ISBN: 978-3-642-24965-5
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