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Modeling Complex Adaptive Systems using Learning Fuzzy Cognitive Maps
| 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
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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