A Hybrid Algorithm for Parameter Tuning in Fuzzy Model Identification

被引：0
|
作者：
Johanyak, Zsolt Csaba [1 ]
Papp, Olga [1 ]
机构：
[1] Kecskemet Coll, Fac Mech Engn & Automat, Dept Informat Technol, H-6000 Kecskemet, Hungary
来源：
ACTA POLYTECHNICA HUNGARICA | 2012年 / 9卷 / 06期
关键词：
cross-entropy; hill climbing; fuzzy rule interpolation; fuzzy model identification; RULE INTERPOLATION; SYSTEMS; CRITERIA;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Parameter tuning is an important step in automatic fuzzy model identification from sample data. It aims at the determination of quasi-optimal parameter values for fuzzy inference systems using an adequate search technique. In this paper, we introduce a new hybrid search algorithm that uses a variant of the cross-entropy (CE) method for global search purposes and a hill climbing type approach to improve the intermediate results obtained by CE in each iteration stage. The new algorithm was tested against four data sets for benchmark purposes and ensured promising results.
引用
收藏
页码：153 / 165
页数：13
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