A review on methods and software for fuzzy cognitive maps

被引：181

作者：

Felix, Gerardo ^{[1
]}

Napoles, Gonzalo ^{[2
]}

Falcon, Rafael ^{[3
]}

Froelich, Wojciech ^{[4
]}

Vanhoof, Koen ^{[2
]}

Bello, Rafael ^{[1
]}

机构：

[1] Cent Univ Las Villas, Santa Clara, Cuba

[2] Hasselt Univ, Hasselt, Belgium

[3] Univ Ottawa, Ottawa, ON, Canada

[4] Univ Silesia, Katowice, Poland

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2019年 / 52卷 / 03期

关键词：

Fuzzy cognitive maps; Machine learning; Software tools; HEBBIAN LEARNING ALGORITHM; NEURAL-NETWORKS; TIME-SERIES; EVOLUTIONARY ALGORITHMS; ADAPTIVE ESTIMATION; GENETIC ALGORITHM; PREDICTION; CLASSIFICATION; CONVERGENCE; OPTIMIZATION;

D O I：

10.1007/s10462-017-9575-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community. However, despite substantial advances in the theory and applications of FCMs, there is a lack of an up-to-date, comprehensive presentation of the state-of-the-art in this domain. In this review study we are filling that gap. First, we present basic FCM concepts and analyze their static and dynamic properties, and next we elaborate on existing algorithms used for learning the FCM structure. Second, we provide a goal-driven overview of numerous theoretical developments recently reported in this area. Moreover, we consider the application of FCMs to time series forecasting and classification. Finally, in order to support the readers in their own research, we provide an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.

引用

页码：1707 / 1737

页数：31

共 135 条

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