摘要
提出一种基于Pareto多目标遗传算法生成一组精确性和解释性较好折衷模糊系统的方法。该方法采用模糊聚类算法辨识初始的模糊模型,利用匹茨堡型实数编码的遗传算法对初始模糊模型的结构和参数进行优化,基于NSGA-Ⅱ算法的目标函数同时考虑模型的精确性和解释性;最后,在算法中利用基于相似性的模型简化方法约简模糊系统。利用该方法对两个Benchmark系统进行建模,仿真结果验证了该方法的有效性。
A novel approach to construct accurate and interpretable fuzzy systems based on Pareto multi-objective genetic algorithm is proposed. The approach is composed of two phases： the initial fuzzy system is identified using fuzzy clustering algorithm; the Pittsburgh-style real-coded genetic algorithm is used to optimize the structure and parameters of the fuzzy systems, and the three-objective function based on NSGA-Ⅱ algorithm combines the interpretability indices and the precision index. In order to improve the interpretability of the fuzzy system, the similarity-driven rule base simplification techniques are used to reduce the fuzzy system. The proposed approach is applied to several benchmark problems, and the results show its validity.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2007年第4期430-434,共5页
Journal of Nanjing University of Science and Technology
基金
国家自然科学基金(60474034)
关键词
TS模糊模型
模糊分类系统
模糊聚类
遗传算法
解释性
TS fuzzy model
fuzzy classification systems
fuzzy clustering
genetic algorithm
inter-pretability
