Competitive Learning Methods with Refractory and Creative Approaches

This paper presents two competitive learning methods with the objective of avoiding the initial dependency of weight (reference) vectors. The first is termed the refractory and competitive learning algorithm. The algorithm has a refractory period: Once the cell has fired, a winner unit corresponding to the cell is not selected until a certain amount of time has passed. Thus, a specific unit does not become a winner in the early stage of processing. The second is termed the creative and competitive learning algorithm. The algorithm is presented as follows: First, only one output unit is prepared at the initial stage, and a weight vector according to the unit is updated under the competitive learning. Next, output units are created sequentially to a prespecified number based on the criterion of the partition error, and competitive learning is carried out until the ternimation condition is satisfied. Finally, we discuss algorithms which have little dependence on the initial values and compare them with the proposed algorithms. Experimental results are presented in order to show that the proposed methods are effective in the case of average distortion. key words: competitive learning, neural networks, refractory period, creating mechanism, vector quantization