By Topic

Neural network for combining linear and non-linear modelling of dynamic systems

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

1 Author(s)
P. P. Madsen ; Dept. of Control Eng., Aalborg Univ., Denmark

The purpose of this paper is to develop a method to combine linear models with MLP networks, i.e. to find a method to make a nonlinear and multivariable model that performs at least as good as a linear model, when the training data lacks information. First, the MLP network for predicting the output from a dynamic system is described. Then two methods are proposed to combine linear and nonlinear modelling. The first method is the MLP network with linear path through, and the second method is a linear model with nonlinear error correction. Finally the two methods are tested. A thermal mixing process is used as a test system. This system is a multivariable and nonlinear process. The test is partly based on a simulation of the process and partly on data from a physical process. The results are given and discussed

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994