An adaptive conscientious competitive learning algorithm and its applications

Abstract

An adaptive conscientious competitive learning (ACCL) algorithm is proposed in this paper. The ACCL algorithm can adjust the conscience parameter itself according to the feedback information about the practical winning situation of all neurons during the learning process. The a priori information about the distribution range of the input patterns which is required for the conventional conscientious competitive learning (CCL) algorithm, is no longer required in the ACCL algorithm. The “neurons get stuck” problem of the competitive learning (CL) algorithm and conscientious competitive learning (CCL) algorithm with small conscience parameter is overcome. At the same time, neurons will not be tangled together as in the case of the CCL algorithm with large conscience parameter. The ACCL algorithm is applied to vector quantization (VQ) and probability density function estimation (PDFE). It can generate better results than the conventional CL and CCL algorithms. Experimental results are also included to demonstrate its effectiveness.