scholarly journals Comparison Between Multiple Linear Regression (MLR) Model and Artificial Neural Network(ANN) Model for the Lean Practices of Manufacturing Industries (SMEs) of Gujarat

2017 ◽  
Vol 10 (11) ◽  
pp. 1-6
Author(s):  
S. Y. Jani ◽  
T. N. Desai ◽  
P. H. Darji ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Manufacturing Industries ◽  
Ann Model ◽  
Lean Practices ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlr Model

scholarly journals An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Mingjun Li ◽  
Junxing Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Concrete Dam ◽  
Artificial Neural ◽  
Dam Deformation ◽  
Artificial Neural Network Ann

Deformation predicting models are essential for evaluating the health status of concrete dams. Nevertheless, the application of the conventional multiple linear regression model has been limited due to the particular structure, random loading, and strong nonlinear deformation of concrete dams. Conversely, the artificial neural network (ANN) model shows good adaptability to complex and highly nonlinear behaviors. This paper aims to evaluate the specific performance of the multiple linear regression (MLR) and artificial neural network (ANN) model in characterizing concrete dam deformation under environmental loads. In this study, four models, namely, the multiple linear regression (MLR), stepwise regression (SR), backpropagation (BP) neural network, and extreme learning machine (ELM) model, are employed to simulate dam deformation from two aspects: single measurement point and multiple measurement points, approximately 11 years of historical dam operation records. Results showed that the prediction accuracy of the multipoint model was higher than that of the single point model except the MLR model. Moreover, the prediction accuracy of the ELM model was always higher than the other three models. All discussions would be conducted in conjunction with a gravity dam study.

scholarly journals Comparative performance of multiple linear regression and artificial neural network models in estimating solute-transport parameters

2021 ◽  
Vol 18 (1) ◽  
pp. 27
Author(s):  
Mohammad Abdul Mojid ◽  
A.B.M. Zahid Hossain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Solute Transport ◽  
Multiple Linear Regression ◽  
Ann Model ◽  
Transport Parameters ◽  
Ann Models ◽  
Artificial Neural ◽  
Mlr Model

Indirect estimate of solute-transport parameters through pedo-transfer functions (PTFs) is becoming important due to expensive and time-consuming direct measurement of the parameters for a large number of soils and solutes. This study evaluated the relative performance of PTFs of multiple linear regression (MLR) and Artificial Neural Network (ANN) models in predicting velocity (<em>V</em>), dispersion coefficient (<em>D</em>) and retardation factor (<em>R</em>) of CaCl<sub>2</sub>, NaAsO<sub>2</sub>, Cd(NO<sub>3</sub>)<sub>2</sub>, Pb(NO<sub>3</sub>)<sub>2</sub> and C<sub>9</sub>H<sub>9</sub>N<sub>3</sub>O<sub>2</sub> (carbendazim) in five agricultural soils. <em>V</em>, <em>D</em> and <em>R</em> of the solutes were determined in repacked soil columns under steady-state unsaturated water flow conditions. Textural class, particle size distribution, bulk density, organic carbon, relative pH, clay%, grain size, and uniformity coefficient of the soils were determined. MLR and ANN models were calibrated with the measured data of four soils and verified for another soil. Root-Mean Square Error (RMSE) is significantly smaller (0.015) and modelling efficiency (EF) is significantly larger (0.999) for ANN model than those (0.096 and 0.954, respectively) for MLR model. Negative Mean Absolute Error (MAE) (-0.0002) of MLR model indicates overestimation, while positive MAE (0.00003) of ANN model indicates minimal underestimation. The ANN model is less biased than the MLR model during prediction. Thus, the ANN model can significantly enhance pollution transport prediction through soils with good accuracy.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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