On the non-differentiability of fuzzy logic systems

Dadone, Paolo; VanLandingham, H.F.

doi:10.1109/icsmc.2000.884404

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…It typically expresses an inference such that if a fact (premise, hypothesis, antecedent) is known, then another fact called a conclusion (consequent) can be inferred or derived. Fuzzy logic systems are rule base systems that implements a nonlinear mapping (Dadone and VanLandingham, 2000) between stresses (represented by consequents) and state variables (represented by antecedents). Creating a fuzzy rule based system may be summarized in four basic steps (Ross 1997;Mahabir et al 2003;Singh and Singh 2005):…”

Section: Fuzzy Rule Based System Architecturementioning

confidence: 99%

Prediction of Herbicides Concentration in Streams

Singh¹

2012

Herbicides - Properties, Synthesis and Control of Weeds

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Section: Fuzzy Rule Based System Architecturementioning

confidence: 99%

Prediction of Herbicides Concentration in Streams

Singh¹

2012

Herbicides - Properties, Synthesis and Control of Weeds

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Balancing Accuracy and Interpretability through Neuro-Fuzzy Models for Cardiovascular Risk Assessment

Casalino

Castellano

Kaymak

et al. 2021

2021 IEEE Symposium Series on Computational Intelligence (SSCI)

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A Gradient-Descent-Based Approach for Transparent Linguistic Interface Generation in Fuzzy Models

Chen

Pedrycz

2010

IEEE Trans. Syst., Man, Cybern. B

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Linguistic interface is a group of linguistic terms or fuzzy descriptions that describe variables in a system utilizing corresponding membership functions. Its transparency completely or partly decides the interpretability of fuzzy models. This paper proposes a GRadiEnt-descEnt-based Transparent lInguistic iNterface Generation (GREETING) approach to overcome the disadvantage of traditional linguistic interface generation methods where the consideration of the interpretability aspects of linguistic interface is limited. In GREETING, the widely used interpretability criteria of linguistic interface are considered and optimized. The numeric experiments on the data sets from University of California, Irvine (UCI) machine learning databases demonstrate the feasibility and superiority of the proposed GREETING method. The GREETING method is also applied to fuzzy decision tree generation. It is shown that GREETING generates better transparent fuzzy decision trees in terms of better classification rates and comparable tree sizes.

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On the non-differentiability of fuzzy logic systems

Cited by 3 publications

References 14 publications

Prediction of Herbicides Concentration in Streams

Prediction of Herbicides Concentration in Streams

Balancing Accuracy and Interpretability through Neuro-Fuzzy Models for Cardiovascular Risk Assessment

A Gradient-Descent-Based Approach for Transparent Linguistic Interface Generation in Fuzzy Models

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