A multi-model approach to determine early manufacturing parameters for small-data-set prediction

Constructing an accurate prediction model from a small training data set is an important but difficult task in the field of forecasting. This is because when the data size is small, the incomplete data may mean that the model produced cannot sufficiently represent the true data structure or cause th...

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Bibliographic Details
Published in:International journal of production research 2012-12, Vol.50 (23), p.6679-6690
Main Authors: Li, Der-Chiang, Liu, Chiao-Wen, Chen, Wen-Chih
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Artificial neural networks
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
forecasting
Inventory control, production control. Distribution
Liquid crystal displays
Manufacturing
Mathematical models
multi-model
Neural networks
On-line systems
Operational research and scientific management
Operational research. Management science
Operations research
Predictions
Production management
Regression
Regression analysis
Reliability theory. Replacement problems
small data set
Studies
Support vector machines
Tasks
TFT-LCD
Training
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Summary:Constructing an accurate prediction model from a small training data set is an important but difficult task in the field of forecasting. This is because when the data size is small, the incomplete data may mean that the model produced cannot sufficiently represent the true data structure or cause the model training to be overfitted. To address this issue, this paper presents an approach that combines multiple prediction models to extract data information in multiple facets. In the multi-model approach, a compromise weight method is proposed to determine the relative reliability of each of the prediction model. The methods used include multiple regression, artificial neural network, and support vector machines for regression. A thin-film transistor liquid crystal display manufacturing case study is used to illustrate the details of this research. The empirical results not only show that the proposed multi-model can reduce the manufacturing variation and increase the production yield, but also can propose a robust and reliable parameter interval to the online engineers in the early manufacturing stage.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2011.613867