Expert-Based Method of Integrated Waste Management Systems for Developing Fuzzy Cognitive Map

Buruzs, Adrienn; Hatwagner, Miklos F.; Kóczy, László T.

doi:10.1007/978-3-319-12883-2_4

Cited by 8 publications

(7 citation statements)

References 45 publications

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“…The commonly used logistic function (Eq. 2) contains the (shape) parameter, , which is usually set equal to 1, but other values have been used (Buruzs, Hatwágner, & Kóczy, 2015). An informed choice for is critical because changing its value influences the results (Supporting Information S4).…”

Section: Activation Functionmentioning

confidence: 99%

Informing network management using fuzzy cognitive maps

Baker

Holden

Plein

et al. 2018

Biological Conservation

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Predicting how management will affect a network is a key challenge of modern conservation. • Fuzzy cognitive maps are a promising method to predict the outcome of network management. • There are two critical methodological issues with fuzzy cognitive maps. • We describe these issues and show how to overcome them. • We demonstrate how to use a fuzzy cognitive map to inform management on Christmas Island.

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Section: Activation Functionmentioning

confidence: 99%

Informing network management using fuzzy cognitive maps

Baker

Holden

Plein

et al. 2018

Biological Conservation

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“…We notice in Table 2 that the ranges of some concepts values are so wide that it is justified to use small values of in (2) because then the corresponding transformation function is not too steep. However, Table 3 presents, for example, that still small concept values are not obtained.…”

Section: Model Construction Examples On Temporal Fuzzy Cognitivementioning

confidence: 99%

“…Chapter 3 considers the challenges of FCM construction especially when time intervals are involved in these models, and detailed examples are provided. These considerations are based on the FCM models and their computer simulations carried out by Niskanen, following the initiatives and monitoring of Minzoni, and at the first stage, also by the work of the Hungarian research group under the supervision of Koczy [2,19]. Chapter 4 concludes our considerations.…”

Section: Introductionmentioning

confidence: 99%

A case study on time-interval fuzzy cognitive maps in a complex organization

Minzoni

Mounoud

Niskanen

2017

2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

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Temporal issues within modeling organizational systems are examined generally and with fuzzy cognitive maps. These maps give the opportunity to consider temporal factors when studying organizational models. The knowledge we gain about the system is useful when the aim is not to optimize time intervals in well-known and instrumented contexts, but also to discover the behavior of the system while different temporal factors are implemented by the management. We will present an adapted resolution for including these factors as key elements in organizational models with fuzzy cognitive map examples for middle and back office application.

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“…provide massive and valuable data samples for research on the formation of haze-fog. The FCM model can be automatically obtained by learning the data [16][17][18][19], and it has been applied in fault diagnosis, medical prediction, pollution management, and intelligent analysis of social phenomena and so on [20][21][22][23][24]. Thus, training of nonlinear and dynamic haze-fog formation time series data to mine the fuzzy cognitive association values becomes an immediate scientific issue to be solved for the formation of haze-fog.…”

Section: Introductionmentioning

confidence: 99%

A New Perspective on Formation of Haze-Fog: The Fuzzy Cognitive Map and Its Approaches to Data Mining

Peng

2017

Sustainability

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Abstract:Haze-fog has seriously hindered the sustainable development of the ecological environment and caused great harm to the physical and mental health of residents in China. Therefore, it is important to probe the formation of haze-fog for its early warning and prevention. The formation of haze-fog is, in fact, a fuzzy nonlinear process. The formation of haze-fog is such a complex process that it is difficult to simulate its dynamic evolution using traditional methods, mainly because of the lack of their consideration of the nonlinear relationships. It is, therefore, essential to explore new perspectives on the formation of haze-fog. In this work, previous research on haze-fog formation is summarized first. Second, a new perspective is proposed on the application of fuzzy cognitive map to the formation of haze-fog. Third, a data mining method based on the genetic algorithm is used to discover the causality values of a fuzzy cognitive map (FCM) for haze-fog formation. Finally, simulation results are obtained through an experiment using the fuzzy cognitive map and its data mining method for the formation of haze-fog. The validity of this approach is determined by definition of a simple rule and the Kappa values. Thus, this research not only provides a new idea using FCM modeling the formation of haze-fog, but also uses an effective method of FCM for solving the nonlinear dynamics of the haze-fog formation.

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Expert-Based Method of Integrated Waste Management Systems for Developing Fuzzy Cognitive Map

Cited by 8 publications

References 45 publications

Informing network management using fuzzy cognitive maps

Informing network management using fuzzy cognitive maps

A case study on time-interval fuzzy cognitive maps in a complex organization

A New Perspective on Formation of Haze-Fog: The Fuzzy Cognitive Map and Its Approaches to Data Mining

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