Prediction of Boiler Output Variables Through the PLS Linear Regression Technique
Journal: The International Arab Journal of Information Technology (Vol.8, No. 3)Publication Date: 2011-07-01
Authors : Abdelmalek Kouadri Mimoun Zelmat AlHussein Albarbar;
Page : 260-264
Keywords : Partial least square; principal component analysis; principal component regression; covariance; and predicted residual sum of squares;
Abstract
In this work, we propose to use the linear regression partial least square method to predict the output variables of the RA1G boiler. This method consists in finding the regression of an output block regarding an input block. These two blocks represent the outputs and inputs of the process. A criteria of cross validation, based on the calculation of the predicted residual sum of squares, is used to select the components of the model in the partial least square regression. The obtained results illustrate the effectiveness of this method for prediction purposes
Other Latest Articles
- A Hierarchical K-NN Classifier for Textual Data
- Effect of Weight Assignment in Data Fusion Based Information Retrieval
- A Flexible Design of Network Devices Using Reconfigurable Content Addressable Memory
- Self-organization and Topology's Control for Mobile Ad-hoc Networks
- Speech Segmentation in Synthesized Speech Morphing Using Pitch Shifting
Last modified: 2019-04-28 21:34:21