Abstract
This paper describes a new method for controlling the capacity and for diminishing the number of parasitic fixed points in a Recursive Neural Network RNN. Based on preliminary researches [1] a Recursive Neural Network may be seen as a graph. The matrix of weights W presents certain properties for which it may be called a tetrahedral matrix [2]. The geometrical properties of these kind of matrices may be used for classifying the n-dimensional state-vector space in n classes[2]. In the recall stage, a parameter vector σ may be introduced, which is related with the capacity of the network [3]. It may be shown that the bigger is the value of the i-th component the vector σ the higher became the capacity of the i class of the state-vector space[2]. Once the capacity has been controlled with the parameter σ, we introduce a new parameter that use the statistical deviation of the prototypes to compare them with those that appears as fixed points, eliminating in this way a great number of parasitic fixed points.
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V. Giménez, P. Gómez-Vilda, M. Pérez-Castellanos and V. Rodellar, A New Approach for Finding the Weights in a Neural Network using Graphs, Proc. of the 36th Midwest Symposium on Circuits and Systems, Detroit, August 16–18, 1993, pp. 113–116.
V. Giménez, E. Torrano, P. Gómez-Vilda and M. Pérez-Castellanos, A Class of Recursive Neural Networks Based on Analytic Geometry, Proc. of the International Conference on Brain Processes, Theories and Models. Canary Islands, Spain, November 12–17, 1995. pp.330–339.
V. Giménez, P. Gómez-Vilda, M. Pérez-Castellanos and E. Torrano, A New Approach for improving the capacity limit on a Recursive Neural Network, Proc. of the AMS'94. IASTED, Lugano, Switzerland, June 20–22, 1994, pp. 90–93.
V. Giménez, P. Gómez-Vilda, E. Torrano and M. Pérez-Castellanos, A New Algorithm for Implementing a Recursive Neural Network, Proc. of the IWANN'95 Málaga-Torremolinos, Spain, June 1995, pp. 252–259.
V. Rodellar, P. Gómez, M. Hermida and R. W. Newcomb, An Auditory Neural System for Speech Processing and Recognition, Proceedings of the ICARCV92, Singapore, September 16–18, 1992, pp. INV-6.2.1–5.
Yves Kamp and Martin Hasler, Recursive Neural Networks for Associative Memory, Wiley-Interscience Series in Systems and Optimization, England, 1990, pp. 10–34.
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© 1997 Springer-Verlag Berlin Heidelberg
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Giménez, V., Pérez-Castellanos, M., Rios Carrion, J., de Mingo, F. (1997). Capacity and parasitic fixed points control in a recursive neural network. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032479
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DOI: https://doi.org/10.1007/BFb0032479
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