Convergence analysis of rival penalized competitive learning (RPCL) algorithm

This paper analyzes the convergence of the rival penalized competitive learning (RPCL) algorithm via a cost function. It is shown that as RPCL process decreases the cost to a global minimum, a correct number of weight vectors will converge to each center of the clusters in the sample data respectively, while the others diverge.