Titlebook: Interpretability Issues in Fuzzy Modeling; Jorge Casillas,Oscar Cordón,Luis Magdalena Book 2003 Springer-Verlag Berlin Heidelberg 2003 aut

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Interpretability in Multidimensional Classification task without making explicit use of the data underlying the model. Application of these techniques to a speech classification problem shows that significant reduction in the model dimensionality can be achieved with minimal accuracy loss.

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典型

A Feature Ranking Algorithm for Fuzzy Modelling Problemsustering. We propose the application of the Sequential Backward Selection (SBS) search method to determine the feature ranking by means of different criterion functions. We examined the proposed method and the criterion functions through a comparative analysis.

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Trade-off between approximation accuracy and complexity: TS controller design via HOSVD based complekly contributing ones according to a given threshold. Reducing the number of local models leads directly to complexity reduction. The explicit form in this paper for the proposed reduction can also be applied on polytopic model approximation methods. A detailed illustrative example of a non-linear dynamic model is also given.

FRAX-tool

Singular Value-Based Fuzzy Reduction With Relaxed Normality Conditionlity (RNO) condition is presented to enhance the interpretability of membership functions in situations where the NO condition cannot be strictly satisfied. The price to pay is an increase in the number of reduced rules and errors.