Implementation of a Radial Basis Function Using VHDL
This paper presents the work regarding the implementation of neural network using radial basis function algorithm on very high speed integrated circuit hardware description language (VHDL). It is a digital implementation of neural network. Neural Network hardware has undergone rapid development during the last decade. Unlike the conventional von-Neumann architecture that is sequential in nature, Artificial Neural Networks (ANNs) Profit from massively parallel processing. A large variety of hardware has been designed to exploit the inherent parallelism of the neural network models. 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.
