Geometric Type-1 and Type-2 Fuzzy Logic Systems

“…Due to the high-computational cost of iterative KM algorithms, alternative type-reduction algorithms that are faster in computation and have closed form expressions have been proposed recently in the literature. Some of the computationally effective alternative type-reduction algorithms, many of them are for the defuzzification of Mamdani IT2 FLSs, are Liang-Mendel Unnormalised Method [23], Wu-Mendel Uncertainty Bounds Method [24], Coupland-John Geometric Method [25], Greenfield-Chiclana-Coupland-John Collapsing Method [26], Nie-Tan Method [27].…”

Section: It2-tsk A2-c0 Fuzzy Systemmentioning

confidence: 99%

“…The input-output mappings are used to predict future values of x at x(t+6). The discretisized data is formed using the fourth order Runga-Kutta method and 1000 samples were generated by the (25). The samples were divided into two equal sized groups each contained 500 samples.…”

Section: B Time-series Predictionmentioning

confidence: 99%

A support vector-based interval type-2 fuzzy system

Uslan

Şeker

John

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-In this paper, a new fuzzy regression model that is supported by support vector regression is presented. Type-2 fuzzy systems are able to tackle applications that have significant uncertainty. However general type-2 fuzzy systems are more complex than type-1 fuzzy systems. Support vector machines are similar to fuzzy systems in that they can also model systems that are non-linear in nature. In the proposed model the consequent parameters of type-2 fuzzy rules are learnt using support vector regression and the computational cost is reduced with the use of a closed-form type reduction. Support vector regression improved the generalisation performance of the fuzzy rule-based system in which the fuzzy rules were a set of interpretable IF-THEN rules. The performance of the proposed model was demonstrated by conducting case studies for the non-linear system approximation and prediction of chaotic time series. The model yielded promising results and the simulation results are compared to the results published in the area.

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“…Extensions to the model will include looking at how alternative methods of representing type-2 fuzzy sets (e.g. geometric [17]) could be used to improve the model's description of the uncertainty in the supply chain, and increasing the complexity of the model so that it is possible for individual nodes to have different review periods as often happens in real-world supply chains.…”

Section: Future Workmentioning

confidence: 99%

Inventory optimisation with an Interval Type-2 Fuzzy model

Miller

Góngora

John

2010

International Conference on Fuzzy Systems

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Abstract-The planning of resources within a supply chain can prove to be a deciding factor in the success or failure of an operation. This research continues the authors' previous work using an extended Interval Type-2 Fuzzy Logic supply chain model, with an Evolutionary Algorithm to search for good resource plans. A set of enhanced experiments is conducted to validate our novel approach with optimal configurations, and determine an appropriate Evolutionary Algorithm set up for the given problem.

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Geometric Type-1 and Type-2 Fuzzy Logic Systems

Cited by 290 publications

References 24 publications

Type-2 fuzzy linear systems

Type-2 fuzzy linear systems

A support vector-based interval type-2 fuzzy system

Inventory optimisation with an Interval Type-2 Fuzzy model

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