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Reseach Article

Modeling Complex Adaptive Systems using Learning Fuzzy Cognitive Maps

by Ahmed Tlili, Salim Chikhi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 3
Year of Publication: 2012
Authors: Ahmed Tlili, Salim Chikhi
10.5120/9096-3199

Ahmed Tlili, Salim Chikhi . Modeling Complex Adaptive Systems using Learning Fuzzy Cognitive Maps. International Journal of Computer Applications. 57, 3 ( November 2012), 28-32. DOI=10.5120/9096-3199

@article{ 10.5120/9096-3199,
author = { Ahmed Tlili, Salim Chikhi },
title = { Modeling Complex Adaptive Systems using Learning Fuzzy Cognitive Maps },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 3 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number3/9096-3199/ },
doi = { 10.5120/9096-3199 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:30.641508+05:30
%A Ahmed Tlili
%A Salim Chikhi
%T Modeling Complex Adaptive Systems using Learning Fuzzy Cognitive Maps
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 3
%P 28-32
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents Learning Fuzzy Cognitive Maps (LFCM) as a new paradigm, or approach, for modeling complex adaptive systems (CAS). This technique is the fusion of the advances of the fuzzy logic, formal neural network, and reinforcement learning where they are suitable for modeling systems in artificial life domain of CAS. The FCM structure is similar to a recurrent artificial neural network. The reinforcement learning (RL) gives the explicative frame of entities like environment changing adaptation. A mathematical adaptation of the Q-learning algorithm is discussed and we present in this work an inspired pseudo-hybridization algorithm Q-learning, mainly used in non-linear dynamic systems RL, and the Hebb law for the inference calculus introduced by the cognitive maps. The prey and predator simulation model is shown.

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Index Terms

Computer Science
Information Sciences

Keywords

Complex adaptive system fuzzy cognitive maps reinforcement learning