By Topic

Variable and delay selection using neural networks and mutual information for data-driven soft sensors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Francisco Souza ; Institute for Systems and Robotics (ISR-UC), and Department of Electrical and Computer Engineering (DEEC-UC), University of Coimbra, Pólo II, PT-3030-290 Coimbra, Portugal ; Pedro Santos ; Rui Araújo

This paper proposes a new method for input variable and delay selection (IVDS) for Soft Sensors (SS) design. The IVDS algorithm is composed by the following steps: (1) Time delay selection; (2) Identification and exclusion of redundant variables; (3) Best variables subset selection. The IVDS algorithm proposed in this work performs the delay and variable selection through two distinct methods, mutual information (MI) is applied to delay selection and for variable selection a multilayer perceptron (MLP) based approach is performed. It is shown in the case studies that the application of the delay selection before applying the variable selection increases the generalization of the MLP-model. The algorithm uses the relative variance tracking precision (RV TP) criterion and the mean square error (MSE) to evaluate the precision of soft sensor. Simulation results are presented showing the effectiveness of the method.

Published in:

Emerging Technologies and Factory Automation (ETFA), 2010 IEEE Conference on

Date of Conference:

13-16 Sept. 2010