Multivariate regression model for peak temperatures in massive elements statistically verified by artificial neural networks

2022 ◽  
Vol 316 ◽  
pp. 126072
Author(s):  
Rodrigo Antunes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Regression Model ◽  
Multivariate Regression ◽  
Multivariate Regression Model ◽  
Artificial Neural

Chapter 5. Multivariate Regression using Artificial Neural Networks

2009 ◽  
pp. 244-283
Author(s):  
Jose Manuel Andrade-Garda ◽  
Alatzne Carlosena-Zubieta ◽  
María Paz Gómez-Carracedo ◽  
Marcos Gestal-Pose
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multivariate Regression ◽  
Artificial Neural

scholarly journals Prediction of Carcass Meat Percentage in Young Pigs Using Linear Regression Models and Artificial Neural Networks

2016 ◽  
Vol 16 (1) ◽  
pp. 275-286 ◽  
Author(s):  
Magdalena Szyndler-Nędza ◽  
Robert Eckert ◽  
Tadeusz Blicharski ◽  
Mirosław Tyra ◽  
Artur Prokowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Performance Testing ◽  
Regression Equations ◽  
Young Pigs ◽  
Marquardt Algorithm ◽  
Artificial Neural

Abstract One of the approaches to improving performance testing of pigs is to look for mathematical solutions to increase the accuracy of calculations. This is mainly done through improvement of linear regression equations based on current data on performance tested pigs in Poland. The advances in computer technology and the improvements in mathematical analysis have made it possible to use artificial neural networks (ANNs) for prediction of carcass meat percentage in young pigs. The aim of the study was to compare the potential for live estimation of carcass meat percentage in pigs using two computational methods: linear regression equations and ANNs. The experiment used 654 gilts of six breeds, which were subjected to performance testing and slaughter analysis at the Pig Performance Testing Station (SKURTCh). The collected data were used to train ANNs to estimate carcass meat percentage in young pigs. Training was performed using the Levenberg- Marquardt algorithm. Next, meatiness estimated by ANNs was compared with the results obtained using linear modelling. It is concluded that based on the fattening and slaughter performance test results of live pigs, artificial neural networks (SSN23) are significantly more accurate in estimating carcass meat percentage in young pigs compared to the three-variable linear regression model 1. The difference in meatiness estimation between SSN23 and the four-variable linear regression model 2 was statistically non-significant in most of the breeds except Duroc and Pietrain, where the meatiness of young animals was estimated more accurately by the linear regression model.

scholarly journals Neuron Network Modeling of Intensification of Isogumulone Extraction in a Rotary Pulse Generator

2021 ◽  
pp. 593-603
Author(s):  
Anton Shafrai ◽  
Elena Safonova ◽  
Dmitry Borodulin ◽  
Yana Golovacheva ◽  
Sergey Ratnikov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Regression Model ◽  
Test Data ◽  
Pulse Generator ◽  
Processing Temperature ◽  
Neuron Network ◽  
Artificial Neural

Introduction. Artificial neural networks are a popular tool of contemporary research and technology, including food science, where they can be used to model various technological processes. The present research objective was to develop an artificial neural network capable of predicting the content of isogumulone in a hop extract at given technological parameters of the rotary pulse generator. Study objects and methods. The mathematical modeling was based on experimental data. The isogumulone content in the hop extract I (mg/dm3) served as an output parameter. The input variables included: processing temperature t (°C), rotor speed n (rpm), processing time  (min), and the gap between the rotor teeth and stator s (mm). Results and discussion. The resulting model had the following parameters: two hidden layers, 30 neurons each; neuron activation function – GELU; loss function – MSELoss; learning step – 0.001; optimizer – Adam; L2 regularization at 0.00001; training set of four batches, 16 records each; 9,801 epochs. The accuracy of the artificial neural network (1.67%) was defined as the mean relative error. The error of the regression model was also low (2.85%). The neural network proved to be more accurate than the regression model and had a better ability to predict the value of the output variable. The accuracy of the artificial neural network was higher because it used test data not included in the training. The regression model when tested on test data showed much worse results. Conclusion. Artificial neural networks proved extremely useful as a means of technological modeling and require further research and application.

scholarly journals Artificial neural networks models for rate of penetration prediction in rock drilling

2017 ◽  
Vol 50 (3) ◽  
pp. 252-255
Author(s):  
Hadi Fathipour Azar ◽  
Timo Saksala ◽  
Seyed-Mohammad Esmaiel Jalali
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multivariate Regression ◽  
Construction Projects ◽  
Multivariate Regression Analysis ◽  
Rate Of Penetration ◽  
Feed Pressure ◽  
Structure Reliability ◽  
Artificial Neural ◽  
The Impact

Prediction of the rate of penetration (ROP) is an important task in drilling economical assessments of mining and construction projects. In this paper, the predictability of the ROP for percussive drills was investigated using the artificial neural networks (ANNs) and the linear multivariate regression analysis. The “power pack” frequency, the revolution per minute (RPM), the feed pressure, the hammer frequency, and the impact energy were considered as input parameters. The results indicate that the ANN with the regression model predicts the ROP under different conditions with high accuracy. It also demonstrates that the ANN approach is a beneficial tool that can reduce cost, time and enhance structure reliability.

Simulation of Aerated Lagoon Using Artificial Neural Networks and Multivariate Regression Techniques

2003 ◽  
pp. 437-449
Author(s):  
Karla Patricia Oliveira-Esquerre ◽  
Aline C. da Costa ◽  
Roy Edward Bruns ◽  
Milton Mori
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multivariate Regression ◽  
Regression Techniques ◽  
Artificial Neural
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