A Fuzzy Cognitive Maps Modeling, Learning and Simulation Framework for Studying Complex System

Espinosa

Int. J. Artif. Intell. Tools

et al. 2018

Self Cite

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.

Section: Examining Software Tools Related To Fuzzy Cognitive Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Espinosa

Int. J. Artif. Intell. Tools

et al. 2018

Self Cite

“…Depending on the human interaction, they may be gathered in two major groups: the semi-automated and the fully-automated models. The first ones still require a relatively limited intervention, whereas fully automated approaches are able to compute a FCM model solely based on historical data [11]. Next, a fully automated model which is able to compute the system causality using historical data as learning knowledge is presented.…”

Section: Learning System Causalitymentioning

confidence: 99%

Learning of Fuzzy Cognitive Maps for simulation and knowledge discovery

Procedings of the Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support

Pérez-García

et al. 2013

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In recent years Fuzzy Cognitive Maps (FCM) has become a useful Soft Computing technique for modeling and simulation. They are connectionist and recurrent structures involving concepts describing the system behavior, and causal connections. This paper describes two abstract models based on Swarm Intelligence for learning parameters characterizing FCM, which is a central issue on this field. At the end, we obtain accurate maps, allowing the simulation of the system and also the extraction of relevant knowledge associated with underlying patterns.

“…The first one is a simple development to support group decision making on a qualitative static model, while the second one is a better implementation, but still hard to interact with and it does not have experimental facilities. More recently, León et al [10] proposed the FCM TOOL. It has a nice Graphical User Interface and also includes a learning algorithm for estimating the causal weights.…”

Section: Introductionmentioning

confidence: 99%

A computational tool for simulation and learning of Fuzzy Cognitive Maps

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Bello

et al. 2015

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Abstract-During the last decade Fuzzy Cognitive Maps (FCM)have become a useful tool for solving unstructured problems. In a few words they could be defined as Recurrent Neural Networks for simulating complex systems, where neurons denote concepts, objects or entities of the investigated system. Normally FCM are entirely designed using the best knowledge of a group of experts in a given domain, so frequently learning algorithms for tuning the model parameters are required. Despite the theoretical advances in such fields, the lack of a suitable computational framework for handling FCM-based systems is still an open problem. This paper introduces a novel tool for designing and simulating FCM which gathers several learning algorithms for adjusting the introduced parameters. More specifically, the framework includes supervised and unsupervised learning algorithms for computing the causal weights, algorithms for optimizing the network topology in large FCM (without losing significant information) and also methods for improving the global convergence on continuous FCM. It should be stated that these algorithms are oriented to prediction tasks, but they could be easily extended to other fields. Index Terms -FCM, prediction problems, modeling, learning algorithms, computational framework.