Fuzzy Cognitive Maps for Modeling Complex Systems

León, Maikel; Rodriguez, Ciro; García, María M.; Bello, Rafael; Vanhoof, Koen

doi:10.1007/978-3-642-16761-4_15

Cited by 15 publications

(11 citation statements)

References 5 publications

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“…In case of the BS or the PN it is not evident for non-experts in the field how to construct a model of the system using these approaches [9] [10]. It is also possible, that the single FCM is created by several experts using different approaches.…”

Section: A Advantages Of Fcmmentioning

confidence: 99%

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Three-term relation neuro-fuzzy cognitive maps

Puheim

Vaščák

Madarasz

2014

2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI)

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In this paper, we propose a novel approach to modeling using fuzzy cognitive maps, which we refer to as the Three-Term Relation Neuro-Fuzzy Cognitive Map or simply the TTR NFCM. The proposed method is mostly suited to model complex nonlinear technical systems with dynamic internal characteristics. With this method we aim to solve some of the most critical problems of the conventional fuzzy cognitive maps. We target two of these problems by hybridization with artificial neural networks. First of them is a linear nature of relations between the concepts. The second is a lack of mutual dependence between the relations connecting to the same concept. Finally, we tackle a problem of relation dynamics using an inspiration from the control engineering. While focusing on bringing these advanced additional methods to the design of cognitive maps, we also aim to keep the degree of dependency on expert knowledge on the same level as with the conventional fuzzy cognitive maps. We achieve this by utilizing the machine learning methods. However, since the proposed method is heavily dependent on automated data-driven learning, it is suitable mainly for systems which are well observable and can produce sufficient training datasets.

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Section: A Advantages Of Fcmmentioning

confidence: 99%

“…It is also possible, that the single FCM is created by several experts using different approaches. This possibility is out of the question in case of the PN, because it is not well established how to combine different PNs which describe the same system [9][10].…”

Section: A Advantages Of Fcmmentioning

confidence: 99%

Three-term relation neuro-fuzzy cognitive maps

Puheim

Vaščák

Madarasz

2014

2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI)

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“…So, FCMs are a powerful methodology that can be used for modelling systems, avoiding many of the knowledge extraction problems which are usually present, for example, in rule based systems. For this study we developed a dedicated software tool, and the details are described in [70]. Although it was conceived for general purposes, special options are included and some specific methods are developed to deal with data requirements used in this study.…”

Section: Fig 2 Example Of a CMmentioning

confidence: 99%

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

León¹,

Nápoles²,

Bello³

et al. 2013

IJCIS

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Although the individuals" transport behavioural modelling is a complex task, it can produce a notable social and economic impact. In this paper, Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such complex systems. An automatic approach to extract mental representations from individuals and convert them into computational structures is defined. For the creation of knowledge bases the use of Knowledge Engineering is accounted and later on the data is transferred into structures based on Fuzzy Cognitive Maps. Once the maps are created, their performances get improved through the use of a Particle Swarm Optimisation algorithm as a learning method, readjusting its predicting capacity from stored scenarios, where individuals left their preferences in front of random situations. Another important result is clustering the maps for knowledge discovery. This permits to find useful groups of individuals that policymakers can use for simulating new rules and policies. After related maps are identified, to merge them as a unique structure could benefit for different usages. Therefore an aggregating procedure is elaborated for this task, constituting an alternative approach for selecting a centroid of a specific estimated group, and therefore having, in only one structure, the knowledge and behavioural acting from a collection of individuals. Learning, clustering and aggregation of Fuzzy Cognitive Maps are combined in a cascade experiment, with the intention of describing travellers" behaviour and change trends in different abstraction levels. The results of this approach will help transportation policy decision makers to understand the people"s needs in a better way, consequently will help them actualising different policy formulations and implementations.

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“…Methods of cognitive-simulation for the study of socioeconomic systems are actively used [3,4]. But features of the socioeconomic systems (SES) demand the adaptation of the existing cognitive tools for the solution of structure and behavior modeling problems, foresight of methods and management for their development.…”

Section: Introductionmentioning

confidence: 99%

Cognitive and Simulation Modeling of Socioeconomic Systems

Ginis¹,

Gorelova²,

Kolodenkova³

2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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Abstract-The article describes the cognitive and simulation metamodel and modern cognitive tools developed for modeling socioeconomic systems. The tools are presented by crisp and fuzzy cognitive maps, parametrical vector functional graph, the solution of a problem of definition of sustainability of model is proposed and simulation example. The metamodel can be used to solve problems of modelling the structure and behavior analysis socioeconomic systems, forecasting of ways and development strategies. The fragment of a computing experiment is given

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Fuzzy Cognitive Maps for Modeling Complex Systems

Cited by 15 publications

References 5 publications

Three-term relation neuro-fuzzy cognitive maps

Three-term relation neuro-fuzzy cognitive maps

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

Cognitive and Simulation Modeling of Socioeconomic Systems

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