Pattern Classification using Polynomial Neural Network

Abstract

In this paper we present polynomial neural network (PNN) model using the group method of data handling to generate a nonlinear time series for classification of patterns. The proposed method considers nonlinear characteristics of the datasets and tries to evolve a polynomial using polynomial neural network that will approximate it to arbitrary token values representing the different classes in the dataset. The approach suggested finds the coefficients of PNN model by means of least square estimation technique. The PNN evolves its layers and number of neurons in each layer after evaluating the fitness function till it attains satisfactory result. Empirical result shows that PNN designed classifier performs better than many other classifier models on selected data sets using less number of features