Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods

Mendez, Gerardo M.; Hernandez, M. de los Angeles

doi:10.1016/j.ins.2008.08.008

Cited by 56 publications

(18 citation statements)

References 25 publications

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“…The fuzzification method can be either singleton or T1 non-singleton. In the literature, most of the T1 ANFIS research and applications are based on singleton fuzzification, while T1 non-singleton is rarely used and can be seen in [13]. b) Layer 1: Every node i in this layer has two inputs, the fuzzy input F j (the output from the corresponding node of layer 0) and the corresponding antecedent FS A k .…”

Section: A T1 Anfismentioning

confidence: 99%

An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models

Chen

John

Twycross

et al. 2016

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

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract-In this paper, an extended ANFIS architecture is proposed. By incorporating an extra layer for the fuzzification process, the extended architecture is able to fit both type-1 and interval type-2 models. The learning properties of the proposed architecture based on the least-squares estimate method are studied on selected type-1 and interval type-2 ANFIS models. We show that the least-squares estimate method in general behaves differently for interval type-2 ANFIS models compared to type-1 ANFIS models, producing larger errors for interval type-2 ANFIS.

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Section: A T1 Anfismentioning

confidence: 99%

An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models

Chen

John

Twycross

et al. 2016

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

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“…Specially, interval T2 FLSs (IT2 FLSs) 1 have become very popular recently and have shown great promise to be used in various modeling and control applications with I/O system uncertainties [19]. Therefore, the analysis of IT2 FLSs systems has become a favorite topic for investigation.…”

Section: Introductionmentioning

confidence: 99%

On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling

Biglarbegian

Melek

Mendel

2011

Information Sciences

109

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“…Due to the advantages of interval-valued fuzzy sets, in recent years, researchers have been investigating the properties as well as the numerous applications of interval-valued fuzzy reasoning in various fields such as neural networks, control systems and so on [4,17,21,25]. Deschrijver [8,11] discussed the representation of intuitionistic fuzzy t-norms, t-conorms and some classes of interval-valued t-norms generated using arithmetic operators.…”

Section: Introductionmentioning

confidence: 99%

Robustness of full implication algorithms based on interval-valued fuzzy inference

Luo

Zhang

2015

International Journal of Approximate Reasoning

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In this paper, a new full implication algorithm based on interval-valued fuzzy inference which extends the triple I principle for fuzzy inference based on fuzzy modus ponens and fuzzy modus tollens to the case of interval-valued fuzzy sets is presented. We first give the corresponding interval-valued R-type triple I solutions and then investigate the robustness of triple I algorithms based on interval-valued fuzzy sets for fuzzy inference. The sensitivity of some special algorithms based on four important interval-valued residuated implication is given. It is shown that the robustness of interval-valued full implication algorithms for fuzzy inference directly depends on the selection of interval-valued fuzzy connectives.

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Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods

Cited by 56 publications

References 25 publications

An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models

An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models

On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling

Robustness of full implication algorithms based on interval-valued fuzzy inference

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