CAJ | 학술논문

竞争学习在聚类分析中是一种重要的学习方式，次胜者惩罚竞争学习( RPCL)算法虽能自动选择合理的类别数，但其性能对学习率和惩罚率的取值较敏感，其变种惩罚控制竞争学习( RPCCL)算法将所有的竞争单元当成冗余单元进行惩罚也不合理。文中提出一种可区分惩罚控制竞争学习算法( DRPCCL)。算法中获胜单元的学习率会在迭代过程中自适应调整。同时该算法使用一种可区分惩罚控制机制来区分竞争单元中的冗余单元和正确单元，给予冗余单元较重惩罚，正确单元轻微惩罚，使得算法能自动确定正确类别数和中心点位置。最后通过实验对比分析证明DRPCCL算法的聚类效果比RPCL算法和RPCCL算法更准确。
경쟁학습재취류분석중시일충중요적학습방식，차성자징벌경쟁학습( RPCL)산법수능자동선택합리적유별수，단기성능대학습솔화징벌솔적취치교민감，기변충징벌공제경쟁학습( RPCCL)산법장소유적경쟁단원당성용여단원진행징벌야불합리。문중제출일충가구분징벌공제경쟁학습산법( DRPCCL)。산법중획성단원적학습솔회재질대과정중자괄응조정。동시해산법사용일충가구분징벌공제궤제래구분경쟁단원중적용여단원화정학단원，급여용여단원교중징벌，정학단원경미징벌，사득산법능자동학정정학유별수화중심점위치。최후통과실험대비분석증명DRPCCL산법적취류효과비RPCL산법화RPCCL산법경준학。
Competitive learning is an important approach for clustering analysis. The rival penalized competitive learning ( RPCL) algorithm has the ability of selecting the correct number of clusters automatically, but its performance is sensitive to the selection of learning rate and de-learning rate. In fact, it is unreasonable that all the rival units are treated as redundant units to be penalized in the variant algorithm called rival penalization controlled competitive learning ( RPCCL) . In this paper, a discriminative rival penalization controlled competitive learning ( DRPCCL) is presented. The learning rate of winning units adaptively adjusts during iteration in the proposed method. Meanwhile, a discriminative penalization controlled mechanism is used to discriminate the redundant units and the correct units in the rival units. The correct units and redundant units are given a slight penalization and a heavier penalization respectively, which makes this algorithm get exact number of clusters and reasonable centre of clusters. The experimental result demonstrates that compared with RPCL and RPCCL, DRPCCL achieves more accurate performance.