A New Approach to Design of Interval Type-2 Fuzzy Logic Systems

Rezaee, Babak

doi:10.1109/his.2008.83

Cited by 2 publications

(1 citation statement)

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“…Recently, an approach for automatic extraction of the type-2 fuzzy rules from data space was shown by Rezaee. 21 In this approach, an initial type-1 fuzzy rule model was generated, and then, this rule base was transformed to interval type-2 fuzzy rule base and tuned. This process was performed until a stop criterion was satisfied.…”

Section: Introductionmentioning

confidence: 99%

An optimal design of interval type-2 fuzzy logic system with various experiments including magnetorheological fluid damper

Nguyen

Choi

Nguyen

2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper focuses on optimal design of an interval type-2 fuzzy logic system (IT-2FLS) to cope with uncertainty issue of training set and noisy data. Content of the solution is depicted based on the proposed algorithm to optimally design an IT-2FLS from a dataset, named OD-T2FLS. The major concept of the OD-T2FLS is a combination of a useful method of clustering data space to establish a type-1 fuzzy logic system (T-1FLS) and an appropriate way to transform the T-1FLS into an IT-2FLS as well as to optimally adjust parameters of the IT-2FLS. Firstly, an improved algorithm to establish an adaptive neuro-fuzzy inference system (ANFIS), named IM-ENFS, is presented. Based on the given dataset, clustering in the join input-output data space is realized to establish a cluster-data space. Using the IM-ENFS for this cluster-data space, together with the cluster-data space optimized, an ANFIS having a role as an optimal T1-FLS is also established. Parameters of the optimized T-1FLS are then used to build the initial structure of IT-2FLS. Subsequently, this IT-2FLS is optimally adjusted based on the well-known genetic algorithm. Finally, to demonstrate the effectiveness of the proposed OD-T2FLS, experiments including magnetorheological fluid damper are realized based on two different statuses of data sources, with and without noise.

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Section: Introductionmentioning

confidence: 99%