Efficient design of feedforward network for pattern classification

Abstract

A feedforward neural network is a computing device whose processing units (the nodes) are distributed in adjacent layers connected through unidirectional links (the weights).Feedforward networks are widely used for pattern recognition. Here two feedforward networks are taken into consideration, Multi Layer Perceptron and Radial Basis Network. while designing these networks problem involves in finding the architecture which is efficient in terms of training time. In this paper different data samples will be presented to RBF and Multi Layer network and the best network selection will be done on the basis of minimum time taken by the network for training. Keywords- Feed forward network, Multi Layer Perceptron Neural Networks, Radial Basis Network, Spread. I. INTRODUCTION Pattern recognition is the study of how machines can observe the environment, learn to explore patterns of interest from their background, and make reasonable decisions about the classes of the patterns. the main properties of neural networks are that they have the ability to learn nonlinear input-output relationships, use sequential training procedures, and adapt themselves to the data. the most commonly used neural network family for pattern classification tasks is the feed-forward network, which includes Multi Layer Perceptron(MLP) and Radial-Basis Function (RBF) networks(9). Neural networks can be viewed as massively parallel computing systems consisting of an extremely large number of simple processors with many interconnections. Neural network models attempt to use some organizational principles (such as learning, generalization, adaptivity, fault tolerance and distributed representation. computation) in a network of weighted directed graphs in which the nodes are artificial neurons and directed edges (with weights) are connections between neuron outputs and neuron inputs. The main characteristics of neural networks are that they have the ability to learn complex nonlinear input-output relationships, use sequential training procedures, and adapt themselves to the data.