Title :
Identification research on improved PID neural network and its application
Author :
Shen, Yongjun ; Gu, Xingsheng ; Bao, Qiong
Author_Institution :
Res. Inst. of Autom., East China Univ. of Sci. & Technol., Shanghai
Abstract :
PID neural network (PID-NN) is a new type of dynamic feed-forward network which combines neural network with PID control strategy. It performs a perfect function in process control with the merit of both general PID controller and neural network. In this paper, the concepts of variable integral and partial differential are introduced in the design of hidden-layer of PID-NN to improve the capabilities of neurons. The structure of system identification is analyzed, and the results of simulation with field data of wet FGD indicate the validity and superiority of this improved modeling approach.
Keywords :
feedforward; neurocontrollers; partial differential equations; three-term control; PID control strategy; PID neural network; dynamic feed-forward network; partial differential; variable integral; Automatic control; Automation; Electronic mail; Feedforward neural networks; Feedforward systems; Intelligent control; Neural networks; Neurons; Process control; Three-term control; ID neural network; identification; partial differential; variable integral;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593323