Development of a Committee of Artificial Neural Networks for the Performance Testing of Compressors for Thermal Machines in Very Reduced Times

Abstract This paper presents a new test method able to infer - in periods of less than 7 seconds - the refrigeration capacity of a compressor used in thermal machines, which represents a time reduction of approximately 99.95% related to the standardized traditional methods. The method was developed aiming at its application on compressor manufacture lines and on 100% of the units produced. Artificial neural networks (ANNs) were used to establish a model able to infer the refrigeration capacity based on the data collected directly on the production line. The proposed method does not make use of refrigeration systems and also does not require using the compressor oil.

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Neural network based stochastic design charts for settlement prediction

Canadian Geotechnical Journal ◽

10.1139/t04-096 ◽

2005 ◽

Vol 42 (1) ◽

pp. 110-120 ◽

Cited By ~ 17

Author(s):

M A Shahin ◽

M B Jaksa ◽

H R Maier

Keyword(s):

Neural Networks ◽

Monte Carlo ◽

Artificial Neural Networks ◽

Shallow Foundations ◽

Granular Soils ◽

Traditional Methods ◽

Settlement Prediction ◽

Design Charts ◽

Artificial Neural ◽

Level Of Risk

Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.Key words: settlement prediction, shallow foundations, neural networks, Monte Carlo, stochastic simulation.

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Mature market segmentation: a comparison of artificial neural networks and traditional methods

Neural Computing and Applications ◽

10.1007/s00521-008-0226-y ◽

2008 ◽

Vol 19 (1) ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Enrique Bigné ◽

Joaquin Aldas-Manzano ◽

Inés Küster ◽

Natalia Vila

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Market Segmentation ◽

Traditional Methods ◽

Artificial Neural ◽

Mature Market

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Application of ANN in Milling Process: A Review

Modelling and Simulation in Engineering ◽

10.1155/2011/696275 ◽

2011 ◽

Vol 2011 ◽

pp. 1-7 ◽

Cited By ~ 10

Author(s):

Salah Al-Zubaidi ◽

Jaharah A. Ghani ◽

Che Hassan Che Haron

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Artificial Neural Networks ◽

Nonlinear Problems ◽

Milling Process ◽

Machining Processes ◽

Traditional Methods ◽

Artificial Neural

In recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on the application of artificial neural networks in the milling process for the last decade.

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

Annals of Animal Science ◽

10.1515/aoas-2015-0057 ◽

2016 ◽

Vol 16 (1) ◽

pp. 275-286 ◽

Cited By ~ 1

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.

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