An Improved Coevolution Algorithm for Fuzzy Modelling

Lu, Yifan; Liu, Y.L.; Liang, Yongsheng

doi:10.1007/978-1-4020-3953-9_56

Cited by 1 publication

(2 citation statements)

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“…Although the former is more flexible than the latter, it has often a lot of nonuniform overlapping membership functions to which it is hard to assign an understandable linguistic term. Since all the input variables are not necessarily interactive, an iterative construction algorithm involving building and pruning has been developed in [4], [5]. A hierarchical rule structure, where the number of fuzzy rules grows linearly with the input number, is proposed in [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Multi-Class Datasets Using 2D Membership Functions in TSK Fuzzy System

Kaki¹,

Teshnehlab²,

Shoorehdeli³

2010

IJACT

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Fuzzy modeling of high-dimensional systems is a challenging topic. This study proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. The proposed method works on the fuzzification layer and tries to use two-dimensional membership functions instead of onedimensional ones. This approach reduces fuzzy rule base radically due to using of two-dimensional membership functions which lead to reduction of parameters. The resulting fuzzy system generated by this method has the following distinct features: 1) the fuzzy system is quite simplified; 2) the fuzzy system is interpretable; 3) the dependencies between the inputs and the outputs are clearly shown. This method has successfully been applied to three classification problem and the results are compared with other works.

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Section: Introductionmentioning

confidence: 99%

“…5. In this structure, x 1 and x 2 go to the first FB and x 3 , x 4 second FB and other layers are the same asFigure.1.…”

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confidence: 99%