DocumentCode
1924853
Title
Simulation Study of PID Neural Network Temperature Control System in Plastic Injecting-Moulding Machine
Author
Shu, Huai-lin ; Shu, Hua
Author_Institution
Guangzhou Univ., Guangzhou
Volume
1
fYear
2007
fDate
19-22 Aug. 2007
Firstpage
492
Lastpage
497
Abstract
PID (proportional, integral and derivative) neural network is a special neural network in which the neurons have proportional, integral and derivative input-output functions and was first given by the author in 1997. The simulation about a three-stage heater in a plastic injection machine was introduced in this paper. The temperature control system of the plastic injection machine is a strong coupled multivariable system and the characteristics of the system are analyzed in the paper. The algorithms of PID neural network are given, the VB program of the back propagation algorithm was introduced and the simulation results are shown. The results prove that the PID neural network has perfect decoupling and self-learning control performances.
Keywords
Visual BASIC; backpropagation; control engineering computing; injection moulding; learning systems; multivariable systems; neurocontrollers; plastics industry; self-adjusting systems; temperature control; three-term control; PID control; VB program; back propagation algorithm; coupled multivariable system; decoupling control; neural network; plastic injecting-moulding machine; self-learning control; temperature control system; Control systems; Cybernetics; MIMO; Machine learning; Neural networks; Neurons; Plastics; Temperature control; Temperature sensors; Three-term control; Decoupling control; Multivariable system; PID neural network; Temperature control;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-0973-0
Electronic_ISBN
978-1-4244-0973-0
Type
conf
DOI
10.1109/ICMLC.2007.4370195
Filename
4370195
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