Learning interpretable fuzzy inference systems with FisPro

被引：88

作者：

Guillaume, Serge ^{[1
]}

Charnomordic, Brigitte ^{[2
]}

机构：

[1] Irstea, UMR ITAP, F-34196 Montpellier, France

[2] INRA SupAgro, UMR MISTEA, F-34060 Montpellier, France

来源：

INFORMATION SCIENCES | 2011年 / 181卷 / 20期

关键词：

Fuzzy rule bases; Interpretability; Modeling; Rule induction; Fuzzy partitioning; ORTHOGONAL LEAST-SQUARES; CONSTRAINTS; RULES;

D O I：

10.1016/j.ins.2011.03.025

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Fuzzy inference systems (FIS) are likely to play a significant part in system modeling, provided that they remain interpretable following learning from data. The aim of this paper is to set up some guidelines for interpretable FIS learning, based on practical experience with fuzzy modeling in various fields. An open source software system called FisPro has been specifically designed to provide generic tools for interpretable FIS design and learning. It can then be extended with the addition of new contributions. This work presents a global approach to design data-driven FIS that satisfy certain interpretability and accuracy criteria. It includes fuzzy partition generation, rule learning, input space reduction and rule base simplification. The FisPro implementation is discussed and illustrated through several detailed case studies. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：4409 / 4427

页数：19

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