DocumentCode
723860
Title
Coking flue temperature RBF neural network model
Author
Zhang Li ; Xu Qingyang ; Jin Shibo ; Li Jiangning
Author_Institution
Sch. of Mech., Shandong Univ. at Weihai, Weihai, China
fYear
2015
fDate
23-25 May 2015
Firstpage
5885
Lastpage
5887
Abstract
A modified radial basis function neural networks (RBFNN) model is proposed to solve the control problem that the flue temperature in coke oven usually has the properties of high nonlinearity, large time-delay and multiple disturbances. The proposed method adopts K-means to initialize hidden layer and center parameters of the network. Finally, the production and energy consumption model are built.
Keywords
coke; control nonlinearities; delay systems; neurocontrollers; radial basis function networks; K-means; RBF neural network model; RBFNN model; coke; coking flue temperature; disturbances; energy consumption model; nonlinearity; radial basis function neural network model; time-delay; Adaptation models; Energy consumption; Input variables; Ovens; Production; Radial basis function networks; Coking Flue; Model; Radial Basis Function Neural-Networks (RBF NN); Temperature;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location
Qingdao
Print_ISBN
978-1-4799-7016-2
Type
conf
DOI
10.1109/CCDC.2015.7161862
Filename
7161862
Link To Document