基于抑制式竞争学习机制的模糊聚类算法研究

立足申请人提出的抑制式模糊C-均值聚类算法中引入的竞争学习机制思想，结合国际上对抑制式模糊C-均值聚类算法的理论与应用研究成果，本项目进一步开展基于抑制式竞争学习机制的模糊聚类算法研究。主要研究内容包括：对抑制式模糊C-均值聚类算法中的抑制率Alpha做进一步的探讨；广义模糊C-均值聚类算法的抑制式改进；截集式模糊C-均值聚类算法的抑制式改进；可能性C-均值聚类算法的抑制式改进。 抑制式模糊C-均值聚类算法中引入的竞争学习机制思想已被国际学者应用到一些模糊聚类算法中，也被推广应用到聚类神经网络、学习矢量量化、支持矢量机等方面，其价值获得了国际同行的认可。本项目继续开展这一方面的深入探讨，其成果会进一步促进基于抑制式竞争学习机制的模糊聚类算法的研究，丰富模糊聚类算法的内容，为更好的处理大数据聚类，尤其是图像分割问题提供一种好的选择。因此本项目的研究不仅具有方法论的价值，也具有显著的应用价值。

Based on the competitive learning mechanism introduced in the suppressed fuzzy c-means clustering algorithm we proposed, this project further studies fuzzy clustering algorithms with suppression type competitive learning mechanism by combining the state-of-the-art theory and practical research results. The main research contents included the followings: further discussion on the suppression ratio Alpha in the suppressed fuzzy c-means clustering algorithm; suppression type improvements for generalized fuzzy c-means clustering algorithms; suppression type improvements for cutset type fuzzy c-means clustering algorithm; suppression type improvements for possibility c-means clustering algorithm. The idea of introducing competitive learning mechanism into suppressed fuzzy c-means clustering algorithm has been applied into some fuzzy clustering algorithms by international scholars, and it's application has also been extended to clustering neural network, learning vector quantization, support vector machine, etc. It's value has been recognized by international peers. This project will continue to carry out an in-depth study on this work. The research results will further promote the study of suppression type competitive learning mechanism based fuzzy clustering algorithms, and enrich the content of fuzzy clustering algorithms. It will also provide a good choice for handling large data clustering, especially for image segmentation problems. Therefore, the research work in this project will not only have the methodology value but also have remarkable application value.