scholarly journals Análisis y modelación estadística del proceso de añejamiento de rones en una ronera cubana

2020 ◽  
Vol 17 (2) ◽  
Author(s):  
Beatriz García Castellanos ◽  
Osney Pérez Ones ◽  
Lourdes Zumalacárregui de Cárdenas ◽  
Idania Blanco Carvajal ◽  
Luis Eduardo López de la Maza
Keyword(s):  
Statistical Analysis ◽  
Regression Model ◽  
Mean Square Error ◽  
Aging Time ◽  
Aging Process ◽  
Variable Temperature ◽  
Solar Irradiation ◽  
Mean Square ◽  
Qualitative Variables ◽  
Very High

The rum aging process shows volume losses, called wastage. The operation variables: product, boardwalk, horizontal and vertical positions, date, volume, alcoholic degree, temperature, humidity and aging time, recorded in databases, contain valuable information to study the process. The qualitative variables were processed using Weka 3.8.0 software while the quantitative variables underwent a statistical analysis using Statgraphics Centurion XVII.2. The biggest reductions correspond to barrels located in areas which solar irradiation, favoring the evaporation of the product. The variable temperature and humidity present very high variation coefficients; these factors are uncontrolled so a regulation process is suggested. A regression model was obtained that predicts the losses based on the variables: numerical month volume and aging time with mean square error values (ECM) and R2 of 0.115 and 95.88 % respectively.

scholarly journals Solar Irradiance Prediction using Neural Model

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Raj Kumar Yadav ◽  
Nivedita Sethy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Mean Square Error ◽  
Solar Irradiance ◽  
Neural Model ◽  
Accurate Prediction ◽  
Solar Irradiation ◽  
National Renewable Energy Laboratory ◽  
Mean Square

The accurate prediction of solar irradiation has been a leading problem for better energy scheduling approach. Hence in this paper, an Artificial neural network based solar irradiance is proposed for five days duration the data is obtained from National Renewable Energy Laboratory, USA and the simulation were performed using MATLAB 2013. It was found that the neural model was able to predict the solar irradiance with a mean square error of 0.0355.

Study of Dynamometer Cards Identification Based on Root-Mean-Square Error Algorithm

2017 ◽  
Vol 32 (02) ◽  
pp. 1850004 ◽  
Author(s):  
Tao Ren ◽  
Xiaoqing Kang ◽  
Wen Sun ◽  
Hong Song
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Production Condition ◽  
Oil Production ◽  
Mean Square ◽  
Similarity Matching ◽  
Pumping System ◽  
Sucker Rod ◽  
Very High

The surface dynamometer cards are important working condition data of sucker-rod pumping system. It has a very important practical significance for the analysis of transmission system and the diagnosis of oil production condition of sucker-rod pumping system. The pump dynamometer cards are important reference for the diagnosis of oil production condition, and its key technology is the identification of pump dynamometer cards. A new similar pattern recognition algorithm based on root-mean-square error (RMSE) is proposed, a theoretical model of the similarity matching algorithm based on RMSE is established, and the algorithm is studied and analyzed. The three-dimensional vibration mathematical models for the surface dynamometer cards are created, by which the surface dynamometer cards can be transformed to the pump dynamometer cards. The accuracy, reliability and stability between the algorithm of RMSE similarity matching and the classical algorithms of similarity pattern matching are studied. The research shows that the resistance to the graphics deformation of RMSE algorithm is the highest among all algorithms. The application of RMSE algorithm and classic similarity matching algorithms to the identification of real pump dynamometer cards and the fault diagnosis of oil wells indicates that the RMSE algorithm has very high identification reliability and accuracy. The remarkable feature of the RMSE algorithm is that it has very high identification accuracy for small difference, while the classical similarity matching algorithms do not have this feature.

scholarly journals Proving the Efficiency of Alternative Linear Regression Model Based on Mean Square Error (MSE) and Average Width using Aquaculture Data

2019 ◽  
Vol 8 (2S3) ◽  
pp. 377-381
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Mean Square Error ◽  
Linear Regression Model ◽  
Regression Method ◽  
Alternative Methods ◽  
Linear Regression Method ◽  
Mean Square ◽  
Average Width ◽  
Alternative Method

Multiple linear regressions (MLR) model is an important tool for investigating relationships between several response variables and some predictor variables. This method is very powerful and commonly used in finance, economic, medical, agriculture and many more. The main objective of this paper is to compare mean square error (MSE) and the average width between alternative linear regression models and linear regression model. The alternative method in this study is a combination of four methods, namely multiple linear regression method, the bootstrap method, a robust regression method and fuzzy regression through the construction of algorithms by using SAS software. Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. The results of the study showed a positive improvement for the estimation of parameters generated through these alternative methods

scholarly journals An Estimation of a Hybrid Log-Poisson Regression Using a Quadratic Optimization Program for Optimal Loss Reserving in Insurance

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Apollinaire Woundjiagué ◽  
Martin Le Doux Mbele Bidima ◽  
Ronald Waweru Mwangi
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Hybrid Model ◽  
Poisson Regression ◽  
Estimation Method ◽  
Error Prediction ◽  
Mean Square ◽  
Poisson Regression Model ◽  
Loss Reserving ◽  
The Mean

In this article, we are interested in developing an alternative estimation method of the parameters of the hybrid log-Poisson regression model. In our previous paper, we have proposed a hybrid log-Poisson regression model where we have derived the analytical expression of the fuzzy parameters. We found that the hybrid model provide better results than the classical log-Poisson regression model according to the mean square error prediction and the goodness of fit index. However, nowhere we have taken into account the optimal value of h(α-cut) which is of greatest importance in fuzzy regressions literature. In this paper, we provide an alternative estimation method of our hybrid model using a quadratic optimization program and the optimized h-value (α-cut). The expected value of fuzzy number is used as a defuzzification procedure to move from fuzzy values to crisp values. We perform the hybrid model with the alternative estimation we are suggesting on two different numerical data to predict incremental payments in loss reserving. From the mean square error prediction, we prove that the alternative estimation of the new hybrid model with an optimized h-value predicts incremental payments better than the classical log-Poisson regression model as well as the same hybrid model with analytical estimation of parameters. Hence we have optimized the outstanding loss reserves.

scholarly journals Poisson Regression-Based Mean Estimator

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Usman Shahzad ◽  
Shabnam Shahzadi ◽  
Noureen Afshan ◽  
Nadia H. Al-Noor ◽  
David Anekeya Alilah ◽  
...  
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Random Sampling ◽  
Poisson Regression ◽  
Simple Random Sampling ◽  
Mean Square ◽  
Poisson Regression Model ◽  
The Mean ◽  
Ratio Estimators

The most frequent method for modeling count responses in numerous investigations is the Poisson regression model. Under simple random sampling, this paper offers utilizing Poisson regression-based mean estimator and discovers its associated formula of the mean square error (MSE). The MSE of the proposed estimator is compared to the MSE of traditional ratio estimators in theory. As a result of these evaluations, the proposed estimator has been proven to be more efficient than traditional estimators. Furthermore, the practical results corroborated the theoretical findings.

scholarly journals A Linear Regression Thermal Displacement Lathe Spindle Model

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 949
Author(s):  
Chih-Jer Lin ◽  
Xiao-Yi Su ◽  
Chi-Hsien Hu ◽  
Bo-Lin Jian ◽  
Li-Wei Wu ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Mean Square Error ◽  
Linear Regression Model ◽  
Rise Time ◽  
Thermal Error ◽  
Temperature Data ◽  
Machining Accuracy ◽  
Mean Square ◽  
Thermal Displacement

Thermal error is one of the main reasons for the loss of accuracy in lathe machining. In this study, a thermal deformation compensation model is presented that can reduce the influence of spindle thermal error on machining accuracy. The method used involves the collection of temperature data from the front and rear spindle bearings by means of embedded sensors in the bearing housings. Room temperature data were also collected as well as the thermal elongation of the main shaft. The data were used in a linear regression model to establish a robust model with strong predictive capability. Three methods were used: (1) Comsol was used for finite element analysis and the results were compared with actual measured temperatures. (2) This method involved the adjustment of the parameters of the linear regression model using the indicators of the coefficient of determination, root mean square error, mean square error, and mean absolute error, to find the best parameters for a spindle thermal displacement model. (3) The third method used system recognition to determine similarity to actual data by dividing the model into rise time and stable time. The rise time was controlled to explore the accuracy of prediction of the model at different intervals. The experimental results show that the actual measured temperatures were very close to those obtained in the Comsol analysis. The traditional model calculates prediction error values within single intervals, and so the model was divided to give rise time and stable time. The experimental results showed two error intervals, 19µm in the rise time and 15µm in the stable time, and these findings allowed the machining accuracy to be enhanced.

scholarly journals Avaliação dos produtos de evapotranspiração baseados em sensoriamento remoto MOD16 e GLEAM em sítios de fluxos turbulentos do Programa LBA

2018 ◽  
Vol 40 ◽  
pp. 112
Author(s):  
Adriana Aparecida Moreira ◽  
Daniela Santini Adamatti ◽  
Anderson Luis Ruhoff
Keyword(s):  
Statistical Analysis ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Eddy Covariance ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Large Scale ◽  
Remotely Sensed ◽  
Mean Square

This study aims to evaluate the performance of MOD16 and GLEAM evapotranspiration (ET) datasets in nine eddy covariance monitoring sites. Data from both ET products were downloaded and its daily means calculated. Evapotranspiration estimations were then compared to the observed ET in the eddy covariance monitoring sites from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA). We performed a statistical analysis using the correlation coefficient (R), the root mean square error (RMSE) and BIAS. Results indicate that, in general, both products can represent the observed ET in the eddy covariance flux towers. MOD16 and GLEAM showed similar values to the calculated statistics when ET estimates were compared to observed ET. Model estimates and eddy covariance flux towers are subject to uncertainties that influence the analysis of remotely-sensed ET products.

scholarly journals Model Regresi Semiparametrik Spline untuk D ata Longitudinal pada Kasus Demam Berdarah Dengue di Kota Makassar

2021 ◽  
Vol 3 (1) ◽  
pp. 20
Author(s):  
Syafruddin Side ◽  
Wahidah Sanusi ◽  
Mustati'atul Waidah Maksum
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Cross Validation ◽  
Semiparametric Regression ◽  
Model Estimation ◽  
Mean Square ◽  
Semiparametric Regression Model ◽  
Regression Spline ◽  
Generalized Cross Validation ◽  
Selection Of

Abstrak. Regresi semiparametrik merupakan model regresi yang memuat komponen parametrik dan komponen nonparametrik dalam suatu model. Pada penelitian ini digunakan model regresi semiparametrik spline untuk data longitudinal dengan studi kasus penderita Demam Berdarah Dengue (DBD) di Rumah Sakit Universitas Hasanuddin Makassar periode bulan  Januari sampai bulan Maret 2018. Estimasi model regresi terbaik didapat dari pemilihan titik knot optimal dengan melihat nilai Generalized Cross Validation (GCV) dan Mean Square Error (MSE) yang minimum. Komponen parametrik pada penelitian ini adalah hemoglobin (g/dL) dan umur (tahun), suhu tubuh ( ), trombosit ( ) sebagai komponen nonparametrik dengan nilai GCV minimum sebesar 221,67745153 dicapai pada titik knot yaitu 14,552; 14,987; dan 15,096; nilai MSE sebesar 199,1032; dan nilai koefisien determinasi sebesar 75,3% yang diperoleh dari model regresi semiparametrik spline linear dengan tiga titik knot..Kata Kunci: regresi semiparametrik, spline, knot, Generalized Cross Validation, Demam Berdarah Dengue.Abstract. Semiparametric regression is a regression model that includes parametric and nonparametric components in it. The regression model in this research is spline semiparametric regression with case studies of patients with Dengue Hemorrahagic Fever (DHF) at University of Hasanuddin Makassar Hospital during the period of January to March 2018. The best regression model estimation is obtained from the selection of optimal knot which has minimum Generalized Cross Validation (GCV) and Mean Square Error (MSE). Parametric component in this research is hemoglobin (g/dL) and age (years), body temperature ( ), platelets ( ) as a nonparametric components. The minimum value of GCV is 221,67745153 achieved at the point 14,552; 14,987; and 15,096 knot; MSE value of 199,1032; and the value of coefficient determination is 75,3% obtained from semiparametric regression model linear spline with third point of knots.Keywords: semiparametric regression, spline, knot, Generalized Cross Validation, Dengue Hemorrahagic Fever.

scholarly journals Reference Evapotranspiration Prediction for Smart Irrigation

2020 ◽  
Vol 10 (1) ◽  
pp. 327-333
Keyword(s):  
Linear Regression ◽  
Root Mean Square Error ◽  
Regression Model ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Linear Regression Model ◽  
Mean Square ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Critical Process

Irrigation is the most critical process for agriculture, but irrigation is the largest consumer of fresh water and causes the loss of large quantities because of the inaccuracy in crop water estimation. Our proposed system aims to improve irrigation management by estimating the amount of water needed by the crop accurately and reduces the number of meteorological parameters needed for such estimation. Detection of the reference crop evapotranspiration (ETo) is the most critical process in crop water estimation, that is considered through our proposed solution by implementing machine learning models using neural networks and linear regression to predict daily ETo using climate data like temperature, humidity, wind speed, and solar radiation. Comparing our system results with FAO-56 Penman-Monteith ET0 and cropwat8.0 software as benchmark, show that our proposed system is better than the linear regression model, in terms of determination coefficient (R^2)=.9677 and root mean square error(RMSE) =.1809, while the multiple linear regression model achieved determination coefficient (R^2)=.68 and root mean square error(RMSE) =3.01. Our system then used the predicted ETo and Crop coefficient (Kc) from FAO, to estimate crop evapotranspiration (ETc) for precision irrigation target.

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