Capacity and parasitic fixed points control in a recursive neural network
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  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 . The geometrical properties of these kind of matrices may be used for classifying the n-dimensional state-vector space in n classes. In the recall stage, a parameter vector σ may be introduced, which is related with the capacity of the network . 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. 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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