Learning Fuzzy Cognitive Maps Using Evolutionary Algorithm Based on System Performance Indicators

被引：4

作者：

Poczeta, Katarzyna ^{[1
]}

Kubus, Lukasz ^{[1
]}

Yastrebov, Alexander ^{[1
]}

Papageorgiou, Elpiniki I. ^{[2
]}

机构：

[1] Kielce Univ Technol, Al Tysiaclecia Panstwa Polskiego 7, PL-25314 Kielce, Poland

[2] Technol Educ Inst TEI Cent Greece, 3rd Km Old Natl Rd Lamia Athens, Lamia 35100, Greece

来源：

AUTOMATION 2017: INNOVATIONS IN AUTOMATION, ROBOTICS AND MEASUREMENT TECHNIQUES | 2017年 / 550卷

关键词：

Fuzzy cognitive map; Evolutionary algorithm; System performance indicators; KNOWLEDGE;

D O I：

10.1007/978-3-319-54042-9_55

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 cognitive map (FCM) is a soft computing technique for modeling decision support systems. Construction of the FCM model is based on the selection of concepts important for the analyzed problem and determining significant connections between them. Fuzzy cognitive map can be initialized based on expert knowledge or automatic constructed from data with the use of supervised or evolutionary learning algorithm. FCM models learned from data are much denser than those created by experts. This paper proposes a new evolutionary approach for fuzzy cognitive maps learning based on system performance indicators. The learning process has been carried out with the use of Elite Genetic Algorithm and Individually Directional Evolutionary Algorithm. The developed approach allows to receive FCM model more similar to the reference system than standard methods for fuzzy cognitive maps learning.

引用

页码：554 / 564

页数：11

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