Pattern classification with a codebook-excited neural network

Abstract

A codebook-excited neural network (CENN) is formed by a multi-layer perceptron excited by a set of code vectors. The authors study its discriminant performance and compare it with other models. The performance improvement with the CENN is demonstrated in a number of cases. The CENN has been developed for classification. The multilayer codebook-excited feedforward neural network enhances the separability of patterns due to its nonlinear mapping and achieves a better discriminant performance than the single-layer one. The codebook-excited recurrent neural network exploits the dependent states among observations and forms a contextual compound classifier, which gives improved performance over ordinary classifiers.<>