Abstract
Hopfield neural network (HNN) is an efficient optimization model, but it easily produces random noise. White noise is an ideal model and is more convenient in the mathematical analysis. This paper presents a new model of Hopfield neural network which is called White Noise Hopfield Neural Network. By introducing the white noise to the Hopfield neural network, we analyze the impact of noise on the neural network. The examples of the functional optimization and the traveling salesman problem (TSP) show as long as the appropriate adjustment Signal Noise Ratio (SNR), the performance of Hopfield network model for optimization has been greatly improved.
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© 2010 Springer-Verlag Berlin Heidelberg
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Xu, Y., Li, Y. (2010). The White Noise Impact on the Optimal Performance of the Hopfield Neural Network. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_8
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DOI: https://doi.org/10.1007/978-3-642-14922-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14921-4
Online ISBN: 978-3-642-14922-1
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