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Neural network architecture for process control based on the RTRL algorithm
Author(s) -
Chovan Tibor,
Catfolis Thierry,
Meert Kürt
Publication year - 1996
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690420218
Subject(s) - artificial neural network , computer science , feedforward neural network , feed forward , process (computing) , recurrent neural network , probabilistic neural network , control engineering , control (management) , time delay neural network , control theory (sociology) , artificial intelligence , engineering , operating system
Neural‐network‐based control schemes are generally designed by replacing standard elements of the classic control schemes by feedforward neural networks. The introduction of discrete time recurrent networks, which are inherently dynamic systems, into those schemes can simplify the design of neural controllers. The concept of applying recurrent networks in indirect adaptive control schemes is described. A combined network cluster consisting of the control network and the model network is constructed to allow the use of the real‐time recurrent learning algorithm. To demonstrate the feasibility of the method two simulation examples are presented.