DocumentCode
1800014
Title
An adequate training set for the AIMNC strategy for typical industrial processes
Author
Igic, Jasmin ; Bozic, Milorad
Author_Institution
mtel a.d. Banja Luka, Banja Luka, Bosnia-Herzegovina
fYear
2014
fDate
25-27 Nov. 2014
Firstpage
183
Lastpage
188
Abstract
Here we have discussed how the training data set should be selected for the Approximate Internal Model-based Neural Control (AIMNC) applied to the typical industrial processes. In the considered control strategy only one neural network (NN), Multi Layer NN (MLNN), which is the neural model of the plant, should be trained off-line. An inverse neural controller can be directly obtained from the neural model without necessity of a further training. Simulations demonstrate performance of the AIMNC strategy for NN model obtained with adequate training set.
Keywords
neurocontrollers; process control; AIMNC strategy; approximate internal model; industrial process; inverse neural controller; multiLayer NN; neural network; training set; Approximation methods; Artificial neural networks; Autoregressive processes; Process control; Steady-state; Training; Vectors; Industrial processes; neural networks; nonlinear internal model control; training set;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Network Applications in Electrical Engineering (NEUREL), 2014 12th Symposium on
Conference_Location
Belgrade
Print_ISBN
978-1-4799-5887-0
Type
conf
DOI
10.1109/NEUREL.2014.7011502
Filename
7011502
Link To Document