Abstract
Selecting rational structure is a crucial problem on multi-layer neural network in application. In this paper a novel method is presented to solve this problem. The method breaks through the traditional methods which only determine the hidden structure and also learns the topological connectivity so that the connectivity structure has small world characteristic. The experiments show that the learned small world neural network using our method reduces the learning error and learning time but improves the generalization when compared to the networks of regular connectivity.
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References
Magnitskii, N.A.: Some New Approaches to the Construction and Learning of Artificial Neural Networks. Computational Mathematics and Modeling 12(4), 293–304 (2001)
Torres, J.J., et al.: Influence of Topology on the Performance of a Neural Network. Neurocomputing, 229–234 (2004)
Watts, D., Strogatz, S.: Collective Dynamics of Small-World Networks. Nature 393, 440–442 (1998)
Stone, M.: Cross-validatory Choice and Assessment of Statistical Prediction. Journal of the Royal Statistical Society, Series B 36, 111–147 (1974)
Schwarz, G.: Estimating the Dimension of a Model. Annals of Statistics 6, 461–464 (1978)
Akaike, H.: A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control AC-19, 716–723 (1974)
Murata, N., Yoshizawa, S., Amari, S.: Network Information Criterion—Determining the Number of Hidden Units for an Artificial Neural Network Model. IEEE Transactions on Neural Networks, 865–871 (1994)
Hou, X., Hu, Y., Li, Y., Xu, X.: Rational Structure of Multi-Layer Artificial Neural Network. Journal of Northeastern University 24(1), 35–38 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, S., Luo, S., Li, J. (2006). Building Multi-layer Small World Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_102
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DOI: https://doi.org/10.1007/11759966_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
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