A Comparative Study of Different Learning Rate In Radial Basis Function

This paper presents the work regarding the implementation of radial basis function algorithm on very high speed integrated circuit hardware description language by using Perceptron learning. Neural Network hardware is usually defined as those devices designed to implement neural architectures and learning algorithms. The radial basis function (RBF) network is a two-layer network whose output units form a linear combination of the basis function computed by the hidden unit & hidden unit function is a Gaussian. The radial basis function has a maximum of 1 when its input is 0. As the distance between weight vector and input decreases, the output increases. Thus, a radial basis neuron acts as a detector that produces 1 whenever the input is identical to its weight vector.