Abstract
A proportional learning vector quantization (PLVQ) algorithm has been developed. The algorithm employs a fuzzy learning law to solve the normalization and initialization problems that are encountered in traditional learning vector quantization (LVQ). The performance of the new algorithm has been compared to that of the learning vector quantization (LVQ) and generalized learning vector quantization (GLVQ) methods by two special examples. The results show that the presented method does not only avoid the initialization problem but also solve the normalization problem.
