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
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;
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
DOI :
10.1109/CCDC.2015.7161862
