A novel fuzzy reinforced learning strategy in vector quantisation

Abstract

This paper presents a new approach toward the design of optimised codebooks by vector quantisation (VQ). A strategy of fuzzy k-means reinforced learning (FRL) is proposed which exploits the advantages offered by fuzzy clustering algorithms, competitive learning and knowledge of training vector and codevector configurations. Reinforced learning, which is consisted of attractive factor and repulsive factor, is used as a pre-process before using the conventional VQ algorithm, i.e. fuzzy k-means (FKM) algorithm. At each iteration of RL, codevectors move intelligently and intentionally toward an improved optimum codebook design. This is distinct from the standard FKM in which a random variation is introduced in the movement of the codevectors to escape from local minima. Experiments demonstrate that this results in a more effective representation of the training vectors by the codevectors and that the final codebook is nearer to the optimal solution in applications such as image compression. It has been found that the standard FKM yields improved quality of codebook design in this application when RL is used as a pre-process. The investigations have also indicated that new fuzzy k-means reinforced learning vector quantisation (FRLVQ) strategy is insensitive to the selection of both the initial codebook and a learning rate control parameter, which is the only additional parameter introduced by FRL from the standard FKM.