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
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;
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
DOI :
10.1109/ICMLC.2007.4370195