T-S Fuzzy Modeling Based on Support Vector Learning

In this paper, we propose a novel approach to fast fuzzy modeling based on a new incremental support vector regression (SVR). Firstly a candidate support vectors selection strategy based on kernel Mahalanobis distance measurement is proposed. This strategy is further used to develop a new incremental learning algorithm to speed up the training process of SVR. Then a hybrid kernel function is utilized to represent an SVR model as a TS fuzzy model. Finally a set of fuzzy rules can be directly extracted from the learning results of SVR. Experimental results of two benchmark examples show that the proposed model not only possesses satisfactory accuracy and generalization ability but also costs less computational time.

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TS-fuzzy modeling based on ε-insensitive smooth support vector regression

Journal of Intelligent & Fuzzy Systems ◽

10.3233/ifs-2012-0599 ◽

2013 ◽

Vol 24 (4) ◽

pp. 805-817 ◽

Cited By ~ 1

Author(s):

Rui Ji ◽

Yupu Yang ◽

Weidong Zhang

Keyword(s):

Support Vector Regression ◽

Fuzzy Modeling ◽

Support Vector

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A New Fuzzy Modeling Approach Based on Support Vector Regression

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) ◽

10.1109/fskd.2007.78 ◽

2007 ◽

Author(s):

Long Yu ◽

Jian Xiao ◽

Yifeng Bai

Keyword(s):

Support Vector Regression ◽

Fuzzy Modeling ◽

Support Vector ◽

Modeling Approach

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Support vector machine? – not only for MRI-images

10.1055/s-0039-3403050 ◽

2020 ◽

Author(s):

V Vasilevska ◽

K Schlaaf ◽

H Dobrowolny ◽

G Meyer-Lotz ◽

HG Bernstein ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector

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Helicopter main reduction planetary gear fault diagnosis method based on SVDD

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209316 ◽

2020 ◽

Vol 64 (1-4) ◽

pp. 137-145

Author(s):

Yubin Xia ◽

Dakai Liang ◽

Guo Zheng ◽

Jingling Wang ◽

Jie Zeng

Keyword(s):

Fault Diagnosis ◽

Planetary Gear ◽

Gaussian Kernel ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Energy Characteristics ◽

Gear Fault ◽

Channel Information ◽

Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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