Base Vector Learning Mechanism for Fuzzy Model

摘要：

Fuzzy model based on support vector machlne(SVM-based fuzzy model) was proposed in recent years. Although SVM has an excellent generalization performance, it is considered to have lower computation speed, and a large number of support vectors may be found, which leads to a complex fuzzy model with too many rules. To deal with the problem, the paper presents a new approach called base vector learning(BVL) to build fuzzy model. There are two steps in the process of the BVL-based fuzzy modeling. First, the quadratic Renyi entropy is applied to select base vectors, which are used to span a subspace in feature space F. Then, all data are projected onto this subspace where classical algorithms such as classification or regression can be applied. If Gaussian kernel is considered, the structure of BVL is equivalent to fuzzy model. The performance of the proposed learning scheme is illustrated by experiments of classification and regression.

关键词： support vector machine fuzzy model kernel function time series prediction

作者: Yugang Fan Hua Wang Haiqing Wang Jiande Wu

作者单位: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kun Faculty of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming, National Lab of Industrial Control Technology and Institute of Industrial Process Control, Zhejiang

会议类型: 国际会议

会议名称: 2010 International Conference on Digital Manufacturing and Automation(2010 数字制造与自动化国际会议 ICDMA 2010)

会议地点: 长沙

会议语种:英文

页码: 303-306

在线出版日期: 2010-12-18（万方平台首次上网日期，不代表论文的发表时间）