Fuzzy Cognitive Maps Based Models for Pattern Classification: Advances and Challenges

2017 ◽  
pp. 83-98 ◽  
Author(s):  
Gonzalo Nápoles ◽  
Maikel Leon Espinosa ◽  
Isel Grau ◽  
Koen Vanhoof ◽  
Rafael Bello
Keyword(s):  
Pattern Classification ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps

scholarly journals FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps

2018 ◽  
Vol 27 (07) ◽  
pp. 1860010 ◽  
Author(s):  
Gonzalo Nápoles ◽  
Maikel Leon Espinosa ◽  
Isel Grau ◽  
Koen Vanhoof
Keyword(s):  
Pattern Classification ◽  
Scenario Analysis ◽  
Cognitive Modeling ◽  
Software Tool ◽  
Cognitive Maps ◽  
Machine Learning Algorithms ◽  
Fuzzy Cognitive Maps ◽  
Software Products ◽  
Knowledge Based ◽  
The Rich

Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many successful studies solved through the use of FCMs; however, when it comes to software implementations, where domain experts can design FCM-based systems, run simulations or perform more advanced experiments, not much is found or documented. The few existing implementations are not proficient in providing options for experimentation. Therefore, we believe that a gap exists, specifically between the theoretical advances and the development of accurate, transparent and sound FCM-based systems; and we advocate for the creation of more complete and exible software products. The goal of this paper is to introduce “FCM Expert”, a software tool for fuzzy cognitive modeling, where we focus on scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters that might define a model, optimize its network topology and improve the system convergence without losing information. Also, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.

Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems

2012 ◽  
Vol 39 (12) ◽  
pp. 10620-10629 ◽  
Author(s):  
G.A. Papakostas ◽  
D.E. Koulouriotis ◽  
A.S. Polydoros ◽  
V.D. Tourassis
Keyword(s):  
Pattern Classification ◽  
Hebbian Learning ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Classification Problems

FUZZY COGNITIVE MAPS FOR PATTERN RECOGNITION APPLICATIONS

2008 ◽  
Vol 22 (08) ◽  
pp. 1461-1486 ◽  
Author(s):  
G. A. PAPAKOSTAS ◽  
Y. S. BOUTALIS ◽  
D. E. KOULOURIOTIS ◽  
B. G. MERTZIOS
Keyword(s):  
Pattern Recognition ◽  
Pattern Classification ◽  
Degrees Of Freedom ◽  
Cognitive Maps ◽  
Classification Problem ◽  
Research Field ◽  
Fuzzy Cognitive Maps ◽  
Classification Problems ◽  
Hybrid Classifier ◽  
Classification Structure

A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.

FUZZY COGNITIVE MAPS EMBEDDED IN A LOW COST AUTONOMOUS MOBILE ROBOT

2019 ◽  
Author(s):  
Márcio Mendonça ◽  
Guilherme Bender Sartori ◽  
Lucas Botoni de Souza ◽  
Giovanni Bruno Marquini Ribeiro
Keyword(s):  
Mobile Robot ◽  
Low Cost ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Autonomous Mobile Robot
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