A new technique for generalized learning vector quantization algorithm

The disadvantage of the generalized learning vector quantization (GLVQ) and fuzzy generalization learning vector quantization (FGLVQ) algorithms is discussed in this paper. And a revised generalized learning vector quantization (RGLVQ) algorithm is proposed to overcome the disadvantage of GLVQ and FGLVQ. Furthermore, by introducing a stimulating coefficient in completing step, a new competing technique to improve the performance of the LVQ neural network is proposed also. The proposed algorithms are tested and evaluated using the IRIS data set. And the efficiency of the proposed algorithms is also illustrated by their use in codebook design for image compression based on vector quantization, and the training time for RGLVQ algorithm is reduced by 10% as compared with FGLVQ while the performance is similar. The new competing technique is also used to generate codebook and PSNR is improved in experiments.