T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

Li, Chaoshun; Zhou, Jianzhong; Xiang, Xiuqiao; Li, Qingqing; An, Xueli

doi:10.1016/j.engappai.2009.02.003

Cited by 100 publications

(52 citation statements)

References 37 publications

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“…The coefficients of consequence part are also affected i.e. one or more coefficients in consequence part are obtained as higher value [15,20]. The proposed algorithm is capable of handling premise distribution data and shows better results.…”

Section: Introductionmentioning

confidence: 95%

TS fuzzy model identification by a novel objective function based fuzzy clustering algorithm

Dam

Deb

2014

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)

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A Fuzzy C Regression Model (FCRM) distance metric has been used in Competitive Agglomeration (CA) algorithm to obtain optimal number rules or construct optimal fuzzy subspaces in whole input output space. To construct fuzzy partition matrix in data space, a new objective function has been proposed that can handle geometrical shape of input data distribution and linear functional relationship between input and output feature space variable. Premise and consequence parameters of Takagi-Sugeno (TS) fuzzy model are also obtained from the proposed objective function. Linear coefficients of consequence part have been determined using the Weighted Recursive Least Square (WRLS) framework. Effectiveness of the proposed algorithm has been validated using a nonlinear benchmark model.

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Section: Introductionmentioning

confidence: 95%

TS fuzzy model identification by a novel objective function based fuzzy clustering algorithm

Dam

Deb

2014

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)

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“…Clusterwise regression analysis is extensively discussed in the theoretical literature (see, for example, [20][21][22]29,30,60,40,31,32,35,25,41,45,51,55,17,33,61,26,27,42,44,46,47,52,54,62,58]), and furthermore, finds application in several fields, such as market segmentation and business, socio-economics, biology, engineering, and so on (see, for instance [2,20,40,57]). …”

Section: Introductionmentioning

confidence: 99%

A class of fuzzy clusterwise regression models

D’Urso

Massari

Santoro

2010

Information Sciences

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“…We get 2000 data points by simulating the boiler-turbine system, which is described earlier, among which the first 1500 samples are treated as training data, and the last 500 as test data. For the purpose of comparison, we select the fuzzy model input signals that are the same as the works of Li et al [8][9][10] Three different fuzzy models M1, M2, and M3 are built to describe drum pressure, electrical output, and drum water level of the original boiler-turbine system separately. For the ith…”

Section: Boiler-turbine Systemmentioning

confidence: 99%

“…However, it was shown that the WRLS algorithm is sensitive to initialization, which leads to no convergence. 8 Hence, different initializations may lead easily to different results. In order to overcome the disadvantage of WRLS, we used the evolutionary computation technique based on ACPSO to initialize the WRLS algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The FCRM clustering algorithm was used to construct c fuzzy rules, and a supervised learning algorithm was adopted to further adjust the obtained parameters and to improve the modeling accuracy. Li et al 8 proposed a novel approach for identification of the TS fuzzy model, which is based on a FCRM clustering algorithm. The clustering prototype in a fuzzy space partition is hyperplane; thus, the FCRM clustering technique is presented, which is deduced from the fuzzy clustering objective function of FCRM with the Lagrange multiplier rule, possessing integrative, and concise structure.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy C‐regression model algorithm using a new PSO algorithm

Adel

Soltani

Châari

2017

Adaptive Control & Signal

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SummaryIn this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear Takagi-Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi-Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. KEYWORDSchaos adaptive particle swarm optimization, fuzzy C-regression model clustering algorithm, identification, multiple-input multiple-output, Takagi-Sugeno fuzzy models

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T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

Cited by 100 publications

References 37 publications

TS fuzzy model identification by a novel objective function based fuzzy clustering algorithm

TS fuzzy model identification by a novel objective function based fuzzy clustering algorithm

A class of fuzzy clusterwise regression models

A fuzzy C‐regression model algorithm using a new PSO algorithm

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