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 :
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