scholarly journals Robust Regression Analysis with LR-Type Fuzzy Input Variables and Fuzzy Output Variable

2016 ◽  
Vol 04 (02) ◽  
pp. 64-80
Author(s):  
Dan Zhang ◽  
Qiujun Lu
Keyword(s):  
Regression Analysis ◽  
Robust Regression ◽  
Output Variable ◽  
Fuzzy Input ◽  
Input Variables

scholarly journals Fuzzy robust regression analysis for fuzzy input- output data

2015 ◽  
Vol 6 (2(26)) ◽  
pp. 4
Author(s):  
Вера Ильинична Грицюк
Keyword(s):  
Regression Analysis ◽  
Robust Regression ◽  
Input Output ◽  
Output Data ◽  
Fuzzy Input

scholarly journals A fuzzy system for cloacal temperature prediction of broiler chickens

2012 ◽  
Vol 42 (1) ◽  
pp. 166-171 ◽  
Author(s):  
Leandro Ferreira ◽  
Tadayuki Yanagi Junior ◽  
Wilian Soares Lacerda ◽  
Giovanni Francisco Rabelo
Keyword(s):  
Decision Support ◽  
Fuzzy System ◽  
Broiler Chickens ◽  
Broiler Chicken ◽  
Fuzzy Inference ◽  
Air Velocity ◽  
Output Variable ◽  
Average Standard Deviation ◽  
Inference System ◽  
Input Variables

Cloacal temperature (CT) of broiler chickens is an important parameter to classify its comfort status; therefore its prediction can be used as decision support to turn on acclimatization systems. The aim of this research was to develop and validate a system using the fuzzy set theory for CT prediction of broiler chickens. The fuzzy system was developed based on three input variables: air temperature (T), relative humidity (RH) and air velocity (V). The output variable was the CT. The fuzzy inference system was performed via Mamdani's method which consisted in 48 rules. The defuzzification was done using center of gravity method. The fuzzy system was developed using MAPLE® 8. Experimental results, used for validation, showed that the average standard deviation between simulated and measured values of CT was 0.13°C. The proposed fuzzy system was found to satisfactorily predict CT based on climatic variables. Thus, it could be used as a decision support system on broiler chicken growth.

scholarly journals Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network

2014 ◽  
Vol 5 (1) ◽  
pp. 361
Author(s):  
A Haris Rangkuti
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fuzzy Neural Network ◽  
Classification Methods ◽  
Neural Network Classification ◽  
Fuzzy Input ◽  
Fuzzy Neural ◽  
Input Variables

 This paper introduces a classification of the image of the batik process, which is based on the similarity of the characteristics, by combining the method of wavelet transform Daubechies type 2 level 2, to process the characteristic texture consisting of standard deviation, mean and energy as input variables, using the method of Fuzzy Neural Network (FNN). Fuzzyfikasi process will be carried out all input values with five categories: Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be a fuzzy input in the process of neural network classification methods. The result will be a fuzzy input in the process of neural network classification methods. For the image to be processed seven types of batik motif is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process with FNN is rule generation, so for the new image of batik can be immediately known motif types after treatment with FNN classification.  For the degree of precision of this method is 86-92%.

scholarly journals Robust Regression Analysis of GCMS Data Reveals Differential Rewiring of Metabolic Networks in Hepatitis B and C Patients

Metabolites ◽  
2017 ◽  
Vol 7 (4) ◽  
pp. 51 ◽  
Author(s):  
Cedric Simillion ◽  
Nasser Semmo ◽  
Jeffrey Idle ◽  
Diren Beyoğlu
Keyword(s):  
Regression Analysis ◽  
Hepatitis B ◽  
Metabolic Networks ◽  
Robust Regression

scholarly journals Incremental Granular Model Improvement Using Particle Swarm Optimization

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 390 ◽  
Author(s):  
Chan-Uk Yeom ◽  
Keun-Chang Kwak
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Numbers ◽  
Particle Swarm ◽  
Housing Price ◽  
Pso Algorithm ◽  
Output Variable ◽  
Swarm Optimization ◽  
Number Of Clusters ◽  
Model Improvement ◽  
Input Variables

This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm. An IGM is a combination of linear regression (LR) and granular model (GM) where the global part calculates the error using LR. However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient. In order to solve these problems, we optimize the number of clusters and their fuzzy numbers according to the characteristics of the data, and use natural imitative optimization PSO algorithm. We further evaluate the performance of the proposed method and the existing IGM by comparing the predicted performance using the Boston housing dataset. The Boston housing dataset contains housing price information in Boston, USA, and features 13 input variables and 1 output variable. As a result of the prediction, we can confirm that the proposed PSO-IGM shows better performance than the existing IGM.

Parameter Estimation in Marketing Models in the Presence of Influential Response Data: Robust Regression and Applications

1984 ◽  
Vol 21 (3) ◽  
pp. 268-277 ◽  
Author(s):  
Vijay Mahajan ◽  
Subhash Sharma ◽  
Yoram Wind
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
Parameter Estimates ◽  
Response Data ◽  
Marketing Models ◽  
Marketing Data

In marketing models, the presence of aberrant response values or outliers in data can distort the parameter estimates or regression coefficients obtained by means of ordinary least squares. The authors demonstrate the potential usefulness of the robust regression analysis in treating influential response values in marketing data.

Adjusting the errors of the GM(1, 2) grey model in the financial data series using an adaptive fuzzy controller

2016 ◽  
Vol 6 (3) ◽  
pp. 341-352 ◽  
Author(s):  
Marcel Bolos ◽  
Ioana Bradea ◽  
Camelia Delcea
Keyword(s):  
Fuzzy Controller ◽  
Financial Data ◽  
Data Series ◽  
Output Variable ◽  
Content Type ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Grey Models ◽  
The Status ◽  
Input Variables

Purpose The purpose of this paper is to focus on the adjustment of the GM(1, 2) errors for financial data series that measures changes in the public sector financial indicators, taking into account that the errors in grey models remain a key problem in reconstructing the original data series. Design/methodology/approach Adjusting the errors in grey models must follow some rules that most often cannot be determined based on the chaotic trends they register in reconstructing data series. In order to ensure the adjustment of these errors, for improving the robustness of GM(1, 2), was constructed an adaptive fuzzy controller which is based on two input variables and one output variable. The input variables in the adaptive fuzzy controller are: the absolute error ε i 0 ( k ) [ % ] of GM(1, 2), and the distance between two values x i 0 ( k ) [ % ] , while the output variable is the error adjustment A ε i 0 ( k ) [ % ] determined with the help of the above-mentioned input variables. Findings The adaptive fuzzy controller has the advantage that sets the values for error adjustments by the intensity (size) of the errors, in this way being possible to determine the value adjustments for each element of the reconstructed financial data series. Originality/value To ensure a robust process of planning the financial resources, the available financial data are used for long periods of time, in order to notice the trend of the financial indicators that need to be planned. In this context, the financial data series could be reconstituted using grey models that are based on sequences of financial data that best describe the status of the analyzed indicators and the status of the relevant factors of influence. In this context, the present study proposes the construction of a fuzzy adaptive controller that with the help of the output variable will ensure the error’s adjustment in the reconstituted data series with GM(1, 2).

Sign in / Sign up

Export Citation Format

Share Document