T–S Fuzzy Model Identification Based on a Novel Hyperplane-Shaped Membership Function

Li, Chaoshun; Zhou, Jianzhong; Li, Chang; Huang, Zhengjun; Zhang, Yongchuan

doi:10.1109/tfuzz.2016.2598850

Cited by 46 publications

(8 citation statements)

References 37 publications

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“…Among them, the Gaussian membership functions are widely used in many areas 28‐30 . However, for the complicated unknown nonlinear systems, the simple fuzzification and defuzzification processes decrease the precision and dynamic quality of fuzzy logic control 31,32 . To improve this situation, a novel kernel function is constructed in this article, which has a good performance in fitting the nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Identification of the nonlinear systems based on the kernel functions

Ding

Yang

2021

Intl J Robust & Nonlinear

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Constructing an appropriate membership function is significant in fuzzy logic control. Based on the multi‐model control theory, this article constructs a novel kernel function which can implement the fuzzification and defuzzification processes and reflect the dynamic quality of the nonlinear systems accurately. Then we focus on the identification problems of the nonlinear systems based on the kernel functions. Applying the hierarchical identification principle, we present the hierarchical stochastic gradient algorithm for the nonlinear systems. Meanwhile, the one‐dimensional search methods are proposed to solve the problem of determining the optimal step sizes. In order to improve the parameter estimation accuracy, we propose the hierarchical multi‐innovation forgetting factor stochastic gradient algorithm by introducing the forgetting factor and using the multi‐innovation identification theory. The simulation example is provided to test the proposed algorithms from the aspects of parameter estimation accuracy and prediction performance.

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Section: Introductionmentioning

confidence: 99%

Identification of the nonlinear systems based on the kernel functions

Ding

Yang

2021

Intl J Robust & Nonlinear

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“…Takagi and Sugeno [1] has demonstrated that systems based on fuzzy rules can approximate highly nonlinear systems. T-S fuzzy model is widely used in nonlinear system modeling and model-based control [2][3][4]. There are many methods to realize premise parameter identification and fuzzy space partition, such as fuzzy c-means (FCM) [5][6][7], fuzzy c-regression model (FCRM) [8][9][10][11][12], Gath-Geva clustering algorithm [13], and Gustafson-Kessel clustering algorithm [14].…”

Section: Introductionmentioning

confidence: 99%

T-S fuzzy systems optimization identification based on FCM and PSO

Ren

Liu

et al. 2020

EURASIP J. Adv. Signal Process.

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The division of fuzzy space is very important in the identification of premise parameters, and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine-tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

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“…Takagi-Sugeno (1985) [1] has demonstrated that systems based on fuzzy rules can approximate highly nonlinear systems. T-S fuzzy model is widely used in nonlinear system modeling and model-based control [2,3,4]. There are many methods to realize premise parameter identification and fuzzy space partition, such as fuzzy c-means (FCM) [5,6,7], fuzzy c-regression model (FCRM) [8,9,10,11,12], Gath-Geva clustering algorithm [13], and Gustafson-Kessel clustering algorithm [14].…”

Section: Introductionmentioning

confidence: 99%

T-S Fuzzy Systems Optimization Identification Based on FCM and PSO

Zhang

Liu

et al. 2020

Preprint

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Abstract The division of fuzzy space is very important in the identification of premise parameters and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

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T–S Fuzzy Model Identification Based on a Novel Hyperplane-Shaped Membership Function

Cited by 46 publications

References 37 publications

Identification of the nonlinear systems based on the kernel functions

Identification of the nonlinear systems based on the kernel functions

T-S fuzzy systems optimization identification based on FCM and PSO

T-S Fuzzy Systems Optimization Identification Based on FCM and PSO

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