Pattern Classification Using Local Linear Wavelet Neural Network

Abstract

In this paper we have used a local linear wavelet neural network (LLWNN) model for pattern classification. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. Particle swarm optimisation (PSO) technique used for training the LLWNN. Simulation results for the classification of different benchmark datasets show the feasibility and effectiveness of the proposed method.