A Learn Of Fuzzy Regression Model and its Applications

Many statistics report shown in fuzzy module into clear problems using the centroid system, consequently we will research the usual linear regression model which is modified from the fuzzy linear regression model. The models enter and generate fuzzy numbers, and the regression coefficients are clear numbers. Hybrid algorithms are considered to fit the fuzzy regression model. So that the validity and quality of the suggested methods can be guaranteed. Therefore,the parameter estimation and have an impact on evaluation situated on knowledge deletion. By way of the gain knowledge of example and evaluation with other model, it may be concluded that the model in this paper is utilized without difficulty and better.

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Fuzzy linear regression model on mulberry silk cocoon characteristics

Research Journal of Textile and Apparel ◽

10.1108/rjta-03-2019-0012 ◽

2019 ◽

Vol 23 (3) ◽

pp. 201-211

Author(s):

Niharendu Bikash Kar ◽

Subhasis Das ◽

Anindya Ghosh ◽

Debamalya Banerjee

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

Fuzzy Regression ◽

Fuzzy Linear Regression ◽

Data Set ◽

Silk Cocoon ◽

Content Type ◽

Fuzzy Regression Model

Purpose This study aims to propose a fuzzy linear regression (FLR) model to deal with the vagueness or fuzziness of the underlying relationship between silk cocoon and yarn quality. Design/methodology/approach Shell ratio percentage, defective cocoon percentage and cocoon volume are considered as significant independent variables to predict the quality of silk cocoons. Input and output parameters of the FLR model are considered as non-fuzzy, but the underlying relationship between the variables is assumed to be fuzzy. Findings The fuzzy regression model shows its superiority against conventional multiple linear regression model for estimation of silk cocoon characteristics. It is inferred that the fuzziness in underlying relationship between the parameters can be handled efficiently by FLR model. Originality/value A rigorous experimental work has been carried out on 40 lots of mulberry silk cocoons to generate real-world data set to characterize silk cocoons’ quality in a fuzzy environment.

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The fuzzy linear regression model for finding sales forcastion from financial leasing in China’s equipment manufacturing industries

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189667 ◽

2021 ◽

Vol 40 (4) ◽

pp. 8477-8484

Author(s):

Chengke Zhu ◽

Junshan Wang

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Substantial Effect ◽

Manufacturing Industries ◽

Fuzzy Linear Regression ◽

Sales Revenue ◽

Financial Leasing ◽

Equipment Manufacturing ◽

Fuzzy Linear Regression Model

With the development of technology and artificial intelligence(AI), financial leasing is used frequently in China’s equipment manufacturing industries, especially after 2008, which attracts our attention. This paper focuses on the problem. A theoretical model is provided in the paper to explain the motivation that why financial leasing is used in equipment manufacturing industries. In this paper, we employ multi-level fuzzy linear regression model to analyze data of manufacturing equipment industry of China in order to discover impact between sales revenue and financial leasing. We also discover credit sale forecasting using this model. We discover that economic leasing has a constructive important effect on sales revenue in total samples, which is consistent with the theoretical framework. However, the results are different in sub-industries, which shows that economic leasing has a constructive and important effect on sales profits in some sub-industries, but some of them are not. Furthermore, we also find that asset characteristics has a substantial effect on financial leasing decision. The outcome demonstrates that the more precise the asset, the easier it can be leased.

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