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Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/305

Title: A Statistical Convergence Aplication for the Hopfield Networks
Authors: Gimenez-Martinez, Victor
Sanchez–Torrubia, Gloria
Torres–Blanc, Carmen
Keywords: Learning Systems
Pattern Recognition
Graph Theory
Recurrent Neural Networks
Issue Date: 2008
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: When Recurrent Neural Networks (RNN) are going to be used as Pattern Recognition systems, the problem to be considered is how to impose prescribed prototype vectors ξ^1,ξ^2,...,ξ^p as fixed points. The synaptic matrix W should be interpreted as a sort of sign correlation matrix of the prototypes, In the classical approach. The weak point in this approach, comes from the fact that it does not have the appropriate tools to deal efficiently with the correlation between the state vectors and the prototype vectors The capacity of the net is very poor because one can only know if one given vector is adequately correlated with the prototypes or not and we are not able to know what its exact correlation degree. The interest of our approach lies precisely in the fact that it provides these tools. In this paper, a geometrical vision of the dynamic of states is explained. A fixed point is viewed as a point in the Euclidean plane R2. The retrieving procedure is analyzed trough statistical frequency distribution of the prototypes. The capacity of the net is improved and the spurious states are reduced. In order to clarify and corroborate the theoretical results, together with the formal theory, an application is presented
URI: http://hdl.handle.net/10525/305
ISSN: 1313-0463
Appears in Collections:Volume 15 Number 1

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