Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Bello

Vanhoof

2013

Self Cite

Abstract. Fuzzy Cognitive Maps (FCM) are a proper knowledge-based tool for modeling and simulation. They are denoted as directed weighted graphs with feedback allowing causal reasoning. According to the transformation function used for updating the activation value of concepts, FCM can be grouped in two large clusters: discrete and continuous. It is notable that FCM having discrete outputs never exhibit chaotic states, but this premise can not be ensured for FCM having continuous output. This paper proposes a learning methodology based on Swarm Intelligence for estimating the most adequate transformation function for each map neuron (concept). As a result, we can obtain FCM showing better stability properties, allowing better consistency in the hidden patterns codified by the map. The performance of the proposed methodology is studied by using six challenging FCM concerning the field of the HIV protein modeling.

Section: ∑ ∑ ∑| |mentioning

confidence: 99%

Learning Stability Features on Sigmoid Fuzzy Cognitive Maps through a Swarm Intelligence Approach

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Bello

Vanhoof

2013

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“…The possibility of aggregating several FCM into a single knowledge structure is an important advantage over other well-known knowledge based approaches such as Bayesian Networks or Petri Nets; since is not obvious for a non-expert users how to combine in a single structure different Bayesian Networks or Petri Nets describing the same system [5].…”

Section: Aggregation Of Fcmmentioning

confidence: 99%

“…In recent works, the modeling of this bioinformatics problem using FCM theory allowed discovering causality patterns on the protease protein, as well as central amino acids [5,23]. Next we will use this model and the continuous learning algorithm for discovering causality between positions of the reverse transcriptase protein and the drug resistance node.…”

Section: First Case Study: Discovering Biological Causality In a Hiv mentioning

confidence: 99%

“…For example, in [4] the authors propose a FCM for studying travel behavior in modern societies allowing policy-makers better understanding of these issues. As another example, [5] describes a model based on FCM for analyzing the dynamics of HIV protease protein; enabling to discover relevant knowledge when a mutation takes place. Also, FCM theory has been widely used in other applications fields including: engineering, risk analysis, business, decision making tasks, management, system control, medicine, game theory, and also telecommunications [6], etc.…”

Section: Introductionmentioning

confidence: 99%

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Learning of Fuzzy Cognitive Maps for simulation and knowledge discovery

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

Grau

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.

“…For instance, León and collaborators [4] proposed a FCM for studying travel behavior in modern societies allowing policy-makers a better understanding about such difficult issues. Similarly, Nápoles et al [5] introduced a FCM-based model for analyzing the resistance mechanism of HIV proteins. This model comprises relevant biological knowledge that could be used by drug-designers when designing new therapies.…”

Section: Introductionmentioning

confidence: 99%

A computational tool for simulation and learning of Fuzzy Cognitive Maps

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

Grau

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.