標題: A new method for constructing membership functions and fuzzy rules from training examples
作者: Wu, TP
Chen, SM
資訊工程學系
Department of Computer Science
關鍵字: fuzzy learning algorithms;fuzzy rules;knowledge acquisition;membership functions;rule-based systems
公開日期: 1-二月-1999
摘要: To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the alpha-cuts of equivalence relations and the alpha-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.
URI: http://dx.doi.org/10.1109/3477.740163
http://hdl.handle.net/11536/31543
ISSN: 1083-4419
DOI: 10.1109/3477.740163
期刊: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume: 29
Issue: 1
起始頁: 25
結束頁: 40
顯示於類別:期刊論文


文件中的檔案:

  1. 000078094200003.pdf