MODELLING OF NEURAL NETWORKS USING LAB VIEW

Abstract

An Artificial Neural network (ANN) is a collection of neurons in a particular arrangement or configuration. It is a parallel processor that can compute or estimate any function. Basically in an ANN, knowledge is stored in memory as experience and is available for use at a future time. The weights associated with the output of each individual neuron represent the memory which stores the knowledge. These weights are also called inter-neuron connection strengths. Each neuron can function locally by itself, but when many neurons act together they can participate in approximating some function. The knowledge that is being stored will also be referred to as “numeric data”, which is transferred between neurons through weights. ANNs learn through training. There are various training algorithms for different types of ANN, based on the specific application. Based on the training, the weights associated with each neuron adjust themselves. By training, it is meant that a set of inputs is presented...