A Generalized Concept for Fuzzy Rule Interpolation

Bárányi, Péter; Kóczy, L.T.; Gedeon, Tom

doi:10.1109/tfuzz.2004.836085

Cited by 215 publications

(145 citation statements)

References 24 publications

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“…Several approaches for reducing fuzzy rule base have been proposed using different techniques such as interpolation methods, orthogonal transformation methods, clustering techniques [3][4][5][6][7][8]. A typical tool to perform model simplification is merging similar fuzzy sets and rules using similarity measures [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Rule Base Reduction Using Conflicting and Reinforcement Measures

Anzilli

Giove

2017

Multidisciplinary Approaches to Neural Computing

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Section: Introductionmentioning

confidence: 99%

Rule Base Reduction Using Conflicting and Reinforcement Measures

Anzilli

Giove

2017

Multidisciplinary Approaches to Neural Computing

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“…That is, the shape differentiation between the resultant fuzzy set and the consequence of the intermediate rule is analogous to the shape differentiation between the observation and the antecedent of the generated intermediate rule. A number of ways to create an intermediate rule and then to infer a conclusion from the given observation by that rule have been developed in the literature, including [4], [5], [21], [22] and [23].…”

Section: Introductionmentioning

confidence: 99%

Towards sparse rule base generation for fuzzy rule interpolation

Tan

Wonders

et al. 2016

2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases by which the entire input domain is fully covered, whilst fuzzy rule interpolation (FRI) is also able to work with sparse rule bases that may not cover certain observations. Thanks to their abilities to work with fewer rules, FRI approaches have also been utilised to reduce system complexity by removing those rules which can be approximated by their neighbouring ones for complex fuzzy models. A number of important fuzzy rule base generation approaches have been proposed in the literature, but the majority of these only target dense rule bases for traditional fuzzy inference systems. This paper proposes a novel sparse fuzzy rule base generation method to support FRI. The approach first identifies important rules that cannot be accurately approximated by their neighbouring ones to initialise the rule base. Then the raw rule base is optimised by fine-tuning the membership functions of the fuzzy sets. Experimentation is conducted to demonstrate the working principles of the proposed system, with results comparable to those of traditional methods.

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“…In [32], an approach is presented to get rid of these disadvantages. It is based on the interpolation of relations instead of interpolating a-cut distances, and which offers a way to derive a family of interpolation methods capable of eliminating some typical deficiencies of fuzzy rule interpolation techniques.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Takagi‐Sugeno fuzzy model for the estimation of nonlinear functions

Jiménez

Al‐Hadithi

Matı́a

et al. 2010

Asian Journal of Control

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Two new and efficient approaches are presented to improve the local and global estimation of the Takagi-Sugeno (T-S) fuzzy model. The main aim is to obtain high function approximation accuracy and fast convergence. The main problem is that the T-S identification method can not be applied when the membership functions are overlapped by pairs. The approaches developed here can be considered as generalized versions of T-S method with optimized performance. The first uses the minimum norm approach to search for an exact optimum solution at the expense of increasing complexity and computational cost. The second is a simple and less computational method, based on weighting of parameters. Illustrative examples are chosen to evalúate the potential, simplicity and remarkable performance of the proposed methods and the high accuracy obtained in comparison with the original T-S model.

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A Generalized Concept for Fuzzy Rule Interpolation

Cited by 215 publications

References 24 publications

Rule Base Reduction Using Conflicting and Reinforcement Measures

Rule Base Reduction Using Conflicting and Reinforcement Measures

Towards sparse rule base generation for fuzzy rule interpolation

Improvement of Takagi‐Sugeno fuzzy model for the estimation of nonlinear functions

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