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.

scholarly journals Study of different data science methods for demand prediction and replenishment forecasting at retail network

2020 ◽  
Author(s):  
Aleksei Iurasov ◽  
Giedre Stanelyte
Keyword(s):  
Fuzzy Logic ◽  
Logistic Regression ◽  
Random Forest ◽  
Linear Regression ◽  
Prediction Model ◽  
Data Science ◽  
Science Methods ◽  
Demand Prediction ◽  
One Step ◽  
Additive Regression

The demand prediction becoming an essential tool to remain or even lead in the competitionamong the retail businesses. A well-done demand prediction model could help retailer to track the level ofinventory, orders and sales in the most effective way in which the best results could be achieved. However,there are many different methods and opinions of how to create a demand prediction model. In this paper,we will analyse the most commonly used methods of Linear regression, Logistic Regression, ProbabilisticNeural Network, Bayesian Additive Regression Trees, Random Forest and Fuzzy Logic with their specificationsand limitations found in studies of authors. After review performed all methods will be compared accordingto characteristics selected. Moreover, in order to get more practical results the accuracy of LogisticRegression and Random Forest methods will be compared based on data of milk sales collected from retailnetwork. For constructing of decision support system for retail network, we need to go beyond demandprediction one-step to replenishment forecasting. It was concluded that there is no best method to forecastreplenishment and results can differ based on the data and conditions analysing. In every situation authorsseeking to select the method with the highest accuracy and the lowest number of errors possible. Limitationsof research: limited number of goods and stores included in the modelling.

scholarly journals A Learn Of Fuzzy Regression Model and its Applications

2019 ◽  
Vol 8 (2) ◽  
pp. 2967-2971
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Fuzzy Numbers ◽  
Fuzzy Regression ◽  
Regression Coefficients ◽  
Fuzzy Linear Regression ◽  
Fuzzy Regression Model ◽  
Fuzzy Linear Regression Model

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.

scholarly journals Multivariate Matrix for Fuzzy Linear Regression Model to Analyse The Taxation in Malaysia

2018 ◽  
Vol 7 (4.33) ◽  
pp. 78
Author(s):  
Noor Hidayah Mohamed Isa ◽  
Mahmod Othman ◽  
Samsul Ariffin Abdul Karim
Keyword(s):  
Linear Regression ◽  
World Bank ◽  
Fuzzy Numbers ◽  
Tax Revenue ◽  
Triangular Fuzzy Numbers ◽  
Fuzzy Linear Regression ◽  
Independent Variables ◽  
Fuzzy Linear Regression Model ◽  
Merchandise Trade ◽  
The Relationship

A multivariate matrix is proposed to find the best factor for fuzzy linear regression (FLR) with symmetric triangular fuzzy numbers (TFNs). The goal of this paper is to select the best factor influence tax revenue among four variables. Eighteen years’ data of the variables from IndexMundi and World Bank Data. It is found that the model is successfully explained between independent variables and response variable. It is notices that  sixty-six percent of the variance of tax revenue is explained by Gross Domestic Product, Inflation, Unemployment and Merchandise Trade. The introduction of multivariate matrix for fuzzy linear regression in taxation is a first attempt to analyses the relationship the tax revenue with the independent variables.  

A fuzzy multiple linear regression model based on meteorological factors for air quality index forecast

2021 ◽  
pp. 1-25
Author(s):  
Yujie Gu ◽  
Yuxiu Zhao ◽  
Jian Zhou ◽  
Hui Li ◽  
Yujie Wang
Keyword(s):  
Air Quality ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Quality Index ◽  
Fuzzy Numbers ◽  
Meteorological Factors ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression

Air quality index (AQI) is an indicator usually issued on a daily basis to inform the public how good or bad air quality recently is or how it will become over the next few days, which is of utmost importance in our life. To provide a more practicable way for AQI prediction, so that residents can clear about air conditions and make further plans, five imperative meteorological indicators are elaborately selected. Accordingly, taking these indicators as independent variables, a fuzzy multiple linear regression model with Gaussian fuzzy coefficients is proposed and reformulated, based on the linearity of Gaussian fuzzy numbers and Tanaka’s minimum fuzziness criterion. Subsequently, historical data in Shanghai from March 2016 to February 2018 are extracted from the government database and divided into two parts, where the first half is statistically analyzed and used for formulating four seasonal fuzzy linear regression models in views of the special climate environment of Shanghai, and the second half is used for prediction to validate the performance of the proposed model. Furthermore, considering that there is beyond dispute that triangular fuzzy number is more prevalent and crucial in the field of fuzzy studies for years, plenty of comparisons between the models based on the two types of fuzzy numbers are carried out by means of the three measures including the membership degree, the fuzziness and the credibility. The results demonstrate the powerful effectiveness and efficiency of the fuzzy linear regression models for AQI prediction, and the superiority of Gaussian fuzzy numbers over triangular fuzzy numbers in presenting the relationships between the meteorological factors and AQI.

scholarly journals Privacy-Preserving Two-Party Computation of Parameters of a Fuzzy Linear Regression

2021 ◽  
pp. 11-19
Author(s):  
Elena Volkova ◽  
◽  
Vladimir Gisin ◽  
Keyword(s):  
Linear Programming ◽  
Linear Regression ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Fuzzy Numbers ◽  
Optimization Problems ◽  
Observation Data ◽  
Fuzzy Linear Regression ◽  
Explanatory Variables ◽  
Common Criterion

Purpose: describe two-party computation of fuzzy linear regression with horizontal partitioning of data, while maintaining data confidentiality. Methods: the computation is designed using a transformational approach. The optimization problems of each of the two participants are transformed and combined into a common problem. The solution to this problem can be found by one of the participants. Results: A protocol is proposed that allows two users to obtain a fuzzy linear regression model based on the combined data. Each of the users has a set of data about the results of observations, containing the values of the explanatory variables and the values of the response variable. The data structure is shared: both users use the same set of explanatory variables and a common criterion. Regression coefficients are searched for as symmetric triangular fuzzy numbers by solving the corresponding linear programming problem. It is assumed that both users are semihonest (honest but curious, or passive and curious), i.e. they execute the protocol, but can try to extract information about the source data of the partner by applying arbitrary processing methods to the received data that are not provided for by the protocol. The protocol describes the transformed linear programming problem. The solution of this problem can be found by one of the users. The number of observations of each user is known to both users. The observation data remains confidential. The correctness of the protocol is proved and its security is justified. Keywords: fuzzy numbers, collaborative solution of a linear programming problem, two-way computation, transformational approach, cloud computing, federated machine learning.

scholarly journals A Systematic Approach to OptimizinghValue for Fuzzy Linear Regression with Symmetric Triangular Fuzzy Numbers

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xilong Liu ◽  
Yizeng Chen
Keyword(s):  
Linear Regression ◽  
Systematic Approach ◽  
Fuzzy Numbers ◽  
Programming Model ◽  
Numerical Study ◽  
Triangular Fuzzy Numbers ◽  
Fuzzy Linear Regression ◽  
The Real ◽  
Optimal Value ◽  
Sample Data

A systematic approach is proposed to optimizehvalue for fuzzy linear regression (FLR) analysis using minimum fuzziness criteria with symmetric triangular fuzzy numbers (TFNs). Firstly, a new concept of credibility is defined to evaluate the performance of FLR models with differenthvalues when a set of sample data pairs is given. Secondly, based on the defined concept of credibility, a programming model is formulated to optimize the value ofh. Finally, both the numerical study and the real application show that the approach proposed in this paper is effective and efficient; that is, optimal value forhcan be determined definitely with respect to a set of given sample data pairs.

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