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A Fuzzy Identification Method Based on the Likelihood Function and Noise Clustering Algorithm
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DOI：10.1007/s40815-022-01366-0.png)
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Abstract
En 中文
In this paper, based on the modified fuzzy c-regression model and noise clustering algorithm, a fuzzy identification method is proposed. Firstly, by considering the relations for the real model, the established model, and the outliers, a modified objective function with noise is proposed to alleviate the affection of noise. Additionally, the consequent parameters of the fuzzy model can be obtained by the iterative formula which obtained by the Lagrangian formula. Furthermore, a modified membership function, which is involved the likelihood function, is propounded to get a more suitable multivariate normal distribution for the data points. Lastly, two examples are illustrated to show the validity and effectiveness of the proposed results.
Keywords:
Noise clustering
Fuzzy c-regression model
Likelihood function
Takagi-Sugeno fuzzy model
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