Clasificador No Lineal Basado en Redes Neuronales con Funciones de Base Radial para Implementación en Sistemas de Punto Fijo
AbstractImplementation of intelligent machines requires of efficient classification systems under limited computational resources. Thisstudy introduces a method for estimating the parameters of Radial Basis Function Neural Network (RBF-NN) that can be implemented on a fixed point processor. First, the number of hidden nodes is chosen based on statistics of the mapped data points. A k-means search is then carried out to determine the location of each node. The hidden units mapping corresponds to the Euclidean distance of their centers to each data point, the weights of the output sum are obtained by solving a linear least squares problem. With this procedure, a low computational cost classifier can be readily implemented on a low capacity platform for real time applications.
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