Research of fuzzy implications via fuzzy linear regression in a eutrophic waterbody

2018 ◽  
Author(s):  
G. Ellina ◽  
G. Papaschinopoulos ◽  
B. K. Papadopoulos
Keyword(s):  
Linear Regression ◽  
Fuzzy Linear Regression ◽  
Fuzzy Implications

scholarly journals The Use of Fuzzy Estimators for the Construction of a Prediction Model Concerning an Environmental Ecosystem

2019 ◽  
Vol 11 (18) ◽  
pp. 5039
Author(s):  
Georgia Ellina ◽  
Garyfalos Papaschinopoulos ◽  
Basil Papadopoulos
Keyword(s):  
Experimental Data ◽  
Fuzzy Logic ◽  
Linear Regression ◽  
Prediction Model ◽  
Shallow Lakes ◽  
Water Body ◽  
Fuzzy Numbers ◽  
Fuzzy Linear Regression ◽  
Fuzzy Implications ◽  
Close Interval

As a variable system, the Lake of Kastoria is a good example regarding the pattern of the Mediterranean shallow lakes. The focus of this study is on the investigation of this lake’s eutrophication, analyzing the relation of the basic factors that affect this phenomenon using fuzzy logic. In the method we suggest, while there are many fuzzy implications that can be used since the proposition can take values in the close interval [0,1], we investigate the most appropriate implication for the studied water body. We propose a method evaluating fuzzy implications by constructing triangular non-asymptotic fuzzy numbers for each of the studied parameters coming from experimental data. This is achieved with the use of fuzzy estimators and fuzzy linear regression. In this way, we achieve a better understanding of the mechanisms and functions that regulate this ecosystem.

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

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.

Fuzzy C-Regression Models

2013 ◽  
Vol 278-280 ◽  
pp. 1323-1326
Author(s):  
Yan Hua Yu ◽  
Li Xia Song ◽  
Kun Lun Zhang
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Statistical Regression ◽  
Fuzzy Linear Regression ◽  
Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

scholarly journals Application of Fuzzy Linear Regression with Symmetric Parameter for Predicting Tumor Size of Colorectal Cancer

2021 ◽  
Vol 9 (1) ◽  
pp. 36-40
Author(s):  
Muhammad Ammar Shafi ◽  
Mohd Saifullah Rusiman ◽  
Siti Nabilah Syuhada Abdullah
Keyword(s):  
Colorectal Cancer ◽  
Linear Regression ◽  
Tumor Size ◽  
Fuzzy Linear Regression
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