A review on methods and software for fuzzy cognitive maps

Felix, Gerardo; Nápoles, Gonzalo; Falcón, Rafael; Froelich, Wojciech; Vanhoof, Koen; Bello, Rafael

doi:10.1007/s10462-017-9575-1

Cited by 200 publications

(133 citation statements)

References 116 publications

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“…As an alternative, we can employ Hebbian-like algorithms which attempt to compute a model with minimal deviation from a predefined initial matrix. Although those methods could be useful in control problems, their poor generalization capability [3] makes them unsuitable for more challenging prediction scenarios.…”

Section: Criticism and Motivationmentioning

confidence: 99%

Short-term cognitive networks, flexible reasoning and nonsynaptic learning

2019

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While the machine learning literature dedicated to fully automated reasoning algorithms is abundant, the number of methods enabling the inference process on the basis of previously defined knowledge structures is scanter. Fuzzy Cognitive Maps (FCMs) are neural networks that can be exploited towards this goal because of their flexibility to handle external knowledge. However, FCMs suffer from a number of issues that range from the limited prediction horizon to the absence of theoretically sound learning algorithms able to produce accurate predictions. In this paper, we propose a neural network system named Shortterm Cognitive Networks that tackle some of these limitations. In our model weights are not constricted and may have a causal nature or not. As a second contribution, we present a nonsynaptic learning algorithm to improve the network performance without modifying the previously defined weights. Moreover, we derive a stop condition to prevent the learning algorithm from iterating without decreasing the simulation error.

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Section: Criticism and Motivationmentioning

confidence: 99%

Short-term cognitive networks, flexible reasoning and nonsynaptic learning

2019

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“…As FCM has been known for many years and has been the subject of many publications, we limit ourselves to a brief presentation of the related procedure. An interested reader can find suitable information about FCM in References [3,9,11,12].…”

Section: The Fcm Proceduresmentioning

confidence: 99%

Evaluation of the Impact of Strategic Offers on the Financial and Strategic Health of the Company—A Soft System Dynamics Approach

Banaś¹,

Michnik²

2019

Mathematics

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When analyzing the possibility of supporting the decision-making process, one should take into account the essential properties of economic entities (the system and its objects). As a result, the development of an effective business model ought to be based on rationality and the characteristics of the system being modeled. Such an approach implies the use of an appropriate analysis and modeling method. Since the majority of relationships in the model are described using the experts’ tacit knowledge, methods known as “soft” are more suitable than “hard” in those situations. Fuzzy cognitive mappings (FCM) are therefore commonly used as a technique for participatory modeling of the system, where stakeholders can convey their knowledge to the model of the system in question. In this study, we introduce a novel approach: the extended weighted influence nonlinear gauge system (WINGS), which may equally well be applied to the decision problems of this type. Appraisal of high-value and long-term offers in the sector of the telecommunication supplier industry serves as a real-world case study for testing the new method. A comparison with FCM provides a deeper understanding of the similarities and differences of the two approaches.

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“…Based on the pioneer work of Axelrod (1976), fuzzy cognitive maps were introduced by Kosko (1986) as a modelling method that can effectively represent causal knowledge and uncertain information of complex systems. The main characteristics of the systems are represented by nodes in a graph, while causality and the strength of the relationship are represented by weighted, directed edges (Felix et al, 2017). Moreover, quick simulation of complex models (Stylios and Groumpos, of the iteration process is used in the representation of the modelled system.…”

Section: Introductionmentioning

confidence: 99%

On the Convergence of Sigmoidal Fuzzy Grey Cognitive Maps

Harmati

Kóczy

2019

International Journal of Applied Mathematics and Computer Science

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Fuzzy cognitive maps (FCMs) are recurrent neural networks applied for modelling complex systems using weighted causal relations. In FCM-based decision-making, the inference about the modelled system is provided by the behaviour of an iteration. Fuzzy grey cognitive maps (FGCMs) are extensions of fuzzy cognitive maps, applying uncertain weights between the concepts. This uncertainty is expressed by the so-called grey numbers. Similarly as in FCMs, the inference is determined by an iteration process which may converge to an equilibrium point, but limit cycles or chaotic behaviour may also turn up. In this paper, based on the grey connections between the concepts and the parameters of the sigmoid threshold function, we give sufficient conditions for the existence and uniqueness of fixed points of sigmoid FGCMs.

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A review on methods and software for fuzzy cognitive maps

Cited by 200 publications

References 116 publications

Short-term cognitive networks, flexible reasoning and nonsynaptic learning

Short-term cognitive networks, flexible reasoning and nonsynaptic learning

Evaluation of the Impact of Strategic Offers on the Financial and Strategic Health of the Company—A Soft System Dynamics Approach

On the Convergence of Sigmoidal Fuzzy Grey Cognitive Maps

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