scholarly journals INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

2012 ◽  
Vol 02 (04) ◽  
pp. 365-370 ◽  
Author(s):  
Saravanan S. ◽  
◽  
Kannan S. ◽  
Thangaraj C. ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Principal Components ◽  
Electricity Demand ◽  
Demand Forecast ◽  
Artificial Neural

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Artificial neural networks and regression analysis for volume estimation in native species

2021 ◽  
Vol 25 (10) ◽  
pp. 664-669
Author(s):  
Lucas M. Amorim ◽  
Elton da S. Leite ◽  
Deoclides R. de Souza ◽  
Liniker F. da Silva ◽  
Carlos R. de Mello ◽  
...  
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Native Species ◽  
Middle Layer ◽  
Volume Measurement ◽  
Volume Estimation ◽  
Individual Volume ◽  
Artificial Neural ◽  
The Individual

ABSTRACT Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.

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