Unsupervised fuzzy competitive learning with monotonically decreasing fuzziness

Despite of its simplicity and success in various applications, conventional competitive learning (CL) making use of the winner-take-all strategy suffers from two major shortcomings, i.e. neuron under utilization and waste of closeness information computed. In this paper, a fuzzy approach to address these shortcomings is pursued. By considering the concept "win" as a fuzzy set, two existing competitive learning algorithms, namely the standard CL algorithm and the frequency sensitive CL algorithm, are generalized and the resulting fuzzy algorithms are proposed. Furthermore, a monotonically decreasing implementation scheme for the fuzziness parameter introduced in the proposed algorithms is suggested to further enhance the overall performance of the fuzzy algorithms. The effectiveness of the proposed algorithms is demonstrated with numerical examples.