Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps

Harmati, István; Kóczy, László T.

doi:10.1007/978-3-030-50153-2_38

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Firstly, the nonlinearity of FCM-based models operating as closed systems makes them difficult to control. Secondly, the most prominent theoretical results reported in the literature [5,20,22] are primarily devoted to deriving sufficient conditions ensuring the existence and uniqueness of the fixed point. These conditions might be quite strict and difficult to fulfill in reality.…”

Section: Fuzzy Cognitive Mapsmentioning

confidence: 99%

Modeling Implicit Bias with Fuzzy Cognitive Maps

Nápoles¹,

Grau²,

Concepción³

et al. 2021

Preprint

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This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete. In our proposal, problem features are mapped to neural concepts that are initially activated by experts when running what-if simulations, whereas weights connecting the neural concepts represent absolute correlation/association patterns between features. In addition, we introduce a new reasoning mechanism equipped with a normalization-like transfer function that prevents neurons from saturating. Another advantage of this new reasoning mechanism is that it can easily be controlled by regulating nonlinearity when updating neurons' activation values in each iteration. Finally, we study the convergence of our model and derive analytical conditions concerning the existence and unicity of fixed-point attractors.

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Section: Fuzzy Cognitive Mapsmentioning

confidence: 99%

Modeling Implicit Bias with Fuzzy Cognitive Maps

Nápoles¹,

Grau²,

Concepción³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Modeling implicit bias with fuzzy cognitive maps

et al. 2022

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Linear Regression Approach to Fuzzy Cognitive Maps with History Data

Niskanen¹

2021

Atlantis Studies in Uncertainty Modelling

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Methods of linear regression analysis are applied to fuzzy cognitive map construction according to history data from the standpoint of quantitative human sciences. When the linearized version of history data is also used, this construction may be reduced to ordinary linear regression analysis. This linearization applies the inverted transformation functions of the fuzzy cognitive maps. Our approach will avoid subjective reasoning and interpretation on these model outcomes by relying on the objective and well-justified statistical theories instead.

show abstract

Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps

Cited by 3 publications

References 14 publications

Modeling Implicit Bias with Fuzzy Cognitive Maps

Modeling Implicit Bias with Fuzzy Cognitive Maps

Modeling implicit bias with fuzzy cognitive maps

Linear Regression Approach to Fuzzy Cognitive Maps with History Data

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