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A fuzzy chance-constrained modelling approach with core philosophies of fuzzy expected value model and fuzzy chance-constrained programming is used in this paper.
Finally, the proposed model and fuzzy optimization approach are applied in a real industrial case.
Based on Thayer's emotion model and Fuzzy Cognitive Maps, this paper presents a proposal for forecasting artificial emotions.
The performance of the developed model is compared with Support Vector Regression model and Fuzzy Support Vector Regression model.
The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model.
For example, the noisy OR-gate model, regression model and fuzzy model are proposed in many literatures [22, 23, 24, 25].
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Comparative analysis shows that, compared with the zero-order fuzzy model, first-order fuzzy model, and polynomial fuzzy model, the proposed model exhibits higher accuracy, better generalization performance, and satisfactory robustness.
In another study, Rossi et al. [21] analyzed and compared random utility models and fuzzy logic models for representing gap acceptance behavior using data from driving simulator experiments.
This paper deals with robust decision making (RDM) for fault detection of an electromechanical system by combining the advantages of Bond Graph (BG) modelling and Fuzzy logic reasoning.
Logic-based models include Boolean logic models, multi-state discrete models, and fuzzy logic models [38], [39].
He discussed and evaluated some of the advanced computational modeling approaches; in particular, state-space modeling, probabilistic Boolean network modeling, and fuzzy logic modeling.
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