期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：3
出版社：IJCSI Press
摘要：Case-based reasoning (CBR) is a useful technique to support decision making (DM) by learning from past experiences. It solves a new problem by retrieving, reusing, and adapting past solutions to old problems that are closely similar to the current problem. In this paper, we combine fuzzy logic with case-based reasoning to identify useful cases that can support the DM. At the beginning, a fuzzy CBR based on both problems and actors similarities is advanced to measure usefulness of past cases. For efficiency, we need an optimal design of membership functions of fuzzy sets. Then, we rely on a meta-heuristic optimization technique i.e. Particle Swarm Optimization to adjust the parameters of the inputs and outputs fuzzy membership functions.
关键词：Decision Making Support; Case;Based Reasoning Fuzzy Logic; Particle Swarm Optimization.
