Implementation of a fast codebook searching algorithm using a neural-network model

The design of a fast codebook searching algorithm using a neural network perceptron model is described. The design of the codebook containing the optimal number of representative vectors incorporates a tree structure with a modified perceptron neuron at each node. The proposed algorithm reduces the computational requirements compared to the full-search and tree-search algorithm and requires less memory than the tree-search algorithm. The implementation of a neural vector pattern matching system using the tree-searched neural vector quantisation algorithm as a building block is discussed briefly