Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

Zhou, Shang‐Ming; Gan, John Q.

doi:10.1016/j.fss.2008.05.016

Cited by 237 publications

(160 citation statements)

References 177 publications

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“…The necessary reduction of rules with weight from the adaptive defuzzification [30][31][32][33]45 , the importance of rule selection [8][9][10][11][12]42,44,48 and the interest in the reduction of rules triggered together 49 , all in order to reduce the system complexity and favour the compactness of the rule base, are the lines followed in our proposal.…”

Section: Description Of the Proposed Mechanismmentioning

confidence: 99%

“…Considering the interpretability and due to its subjective nature and the large amount of factors involved, the choice of appropriate measures is still an open problem 21,28,[30][31][32][33][34] . In the specialist literature, there are proposal of different measures 27,28,[30][31][32][33][34] and techniques [8][9][10]22,23,[35][36][37] for obtaining more interpretable linguistic fuzzy models.…”

Section: Introductionmentioning

confidence: 99%

“…In the specialist literature, there are proposal of different measures 27,28,[30][31][32][33][34] and techniques [8][9][10]22,23,[35][36][37] for obtaining more interpretable linguistic fuzzy models. There are also different proposal for taxonomies [30][31][32][33] to help better understand better the interpretability matter.…”

Section: Introductionmentioning

confidence: 99%

“…They are not the most relevant way to improve the accuracy, but they are well-matched with most other methodologies to do so. However, adaptive defuzzification methods introduce a loss of interpretability 15,[30][31][32][33] , with implications for the overall meaning 15,32 .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm

Márquez¹,

Márquez²,

Peregrín³

2012

IJCIS

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This paper proposes a mechanism that helps improve the interpretability of linguistic fuzzy ruled based systems with common adaptive defuzzification methods. Adaptive defuzzification significantly improves the system accuracy, but introduces weights associated with each rule of the rule base, decreasing the system interpretability. The suggested mechanism is based on three goals: 1) reducing the number of total rules considering that rule weight close to zero can be removed; 2) reducing the rules with weights coupled because rules with weights close to one do not need the weight, and 3) reducing rules triggered jointly, all of them by using several metrics and a proposed interpretability index. This is performed using a multi-objective evolutionary algorithm, obtaining a set of solutions with different trade-offs between accuracy and interpretability. In addition, it is important to note that adaptive defuzzification and therefore the proposal developed in this work can be used together with other methodologies to improve system interpretability and accuracy, so it can be viewed as an interesting component.

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Section: Description Of the Proposed Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm

Márquez¹,

Márquez²,

Peregrín³

2012

IJCIS

View full text Add to dashboard Cite

show abstract

“…It has been extensively studied in the case of fuzzy rule-based systems [1,2,3,4], which have been the most widespread within all fuzzy modelling methods, mainly because of the early success of fuzzy expert systems and fuzzy control, both based on rules of the form "if V 1 is A 1 and V 2 is A 2 , ..., then W is B". These studies point out the difficulty to assess interpretability and provide various kinds of properties involved in its evaluation.…”

Section: Introductionmentioning

confidence: 99%

Interpretability of fuzzy linguistic summaries

Lesot

Moyse

Bouchon‐Meunier

2016

Fuzzy Sets and Systems

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This paper investigates the question of the interpretability of fuzzy linguistic summaries, both at the sentence level and at the summary level, seen as a set of sentences. The individual sentence interpretability is examined as depending both on its representativity measured by a quality degree and on its linguistic expression. Different properties at the summary level are also discussed, namely their consistency, their non redundancy and the information they convey.

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Non‐Rule Based Fuzzy Approach for Adaptive Control Design of Nonlinear Systems

Wang¹,

Luo²,

Novaković³

et al. 2014

Asian Journal of Control

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A novel adaptive control approach is presented using extended fuzzy logic systems without any rules. First, the extended fuzzy logic systems without any rules are used to approximate the uncertainties. Then the sliding mode controllers via the proposed extended fuzzy logic systems without any rules are proposed for uniformly ultimately bounded (UUB) nonlinear systems. The adaptive laws are used for estimating the approximation accuracies of fuzzy logic systems without any rules, Lipschitz constants of uncertain functions and scalar factor, respectively, which are not directly to estimate the coefficients of basis functions. Finally, a compared simulation example is utilized to demonstrate the effectiveness of the approach proposed in this paper.

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Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

Cited by 237 publications

References 177 publications

A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm

A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm

Interpretability of fuzzy linguistic summaries

Non‐Rule Based Fuzzy Approach for Adaptive Control Design of Nonlinear Systems

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