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

2008 ◽  
Vol 19 (1) ◽  
pp. 1-11 ◽  
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

Artificial neural networks: A new methodology for industrial market segmentation

1995 ◽  
Vol 24 (5) ◽  
pp. 431-438 ◽  
Author(s):  
Kelly E Fish ◽  
James H Barnes ◽  
Milam W AikenAssistant
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Market Segmentation ◽  
Industrial Market ◽  
Artificial Neural

Neural network based stochastic design charts for settlement prediction

2005 ◽  
Vol 42 (1) ◽  
pp. 110-120 ◽  
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.

scholarly journals Application of ANN in Milling Process: A Review

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
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.

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

2015 ◽  
Vol 22 (1) ◽  
pp. 79-88 ◽  
Author(s):  
Rodrigo Coral ◽  
Carlos A. Flesch ◽  
Cesar A. Penz ◽  
Maikon R. Borges
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Production Line ◽  
Performance Testing ◽  
Test Method ◽  
Traditional Methods ◽  
Refrigeration Capacity ◽  
Artificial Neural ◽  
Time Reduction ◽  
Compressor Manufacture

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.

THERMAL PROCESS CALCULATION USING ARTIFICIAL NEURAL NETWORKS AND OTHER TRADITIONAL METHODS

2006 ◽  
Vol 29 (2) ◽  
pp. 162-173 ◽  
Author(s):  
ELIANE CALOMINO GONCALVES ◽  
LUIS ANTONIO MINIM ◽  
JANE SELIA DOS REIS COIMBRA ◽  
VALERIA PAULA RODRIGUES MINIM
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermal Process ◽  
Traditional Methods ◽  
Artificial Neural ◽  
Process Calculation
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