Learning algorithm with Gaussian membership function for Fuzzy RBF neural networks

In this paper a new learning algorithm for Fuzzy Radial Basis Function Neural Networks is presented which is characterized by its fully-supervising, self-organizing and fuzzy properties, with an associated computational cost that is fewer than other algorithms. II is intended for pattern classification tasks, and is capable of automatically configuring the Fuzzy RBF network. The methodology shown here is bused on the self-determination of network architecture and the self-recruitment of nodes with a gaussian type of activation function. i.e. the center and covariance matrices of the activation functions together with the number of tuned and output nodes. This approach consists in a mix of the ''Thresholding in Features Spaces'' techniques rind the updating strategies of the ''Fuzzy Kohonen Clustering Networks'' introducing a Gaussian Membership function. Its properties are the same as those of the traditional membership function used in Furry c-Means clustering algorithms, but with the membership function proposed here it lets a nearer relationship exist between learning algorithm and network architecture. Data from a real image and the results given by the algorithm ore used to illustrate this method.