Abstract：According to the process characteristics of the medium thickness steel plate temperature control in the heat treatment furnace, a new steel temperature predictive model of heat treatment furnace based on the Least Squares Support Vector Machine (LS-SVM) of mixtures of kernels is established; the model also gives considerations to the some problems, such as the existence of local minima and the choice of the number of hidden units in traditional neural network. Based on the new model, the design steps are given. The LS-SVM model is able to fit the complex nonlinear functional relation between the input and output. Roll speed, steel plate length, width, thickness, upper and lower temperature of furnace, surface temperature of steel plate in previous moment are selected as inputs, surface temperature of steel plate in this moment is chosen as outputs. The very good forecast effect is obtained when on-site production process data are taken as the training samples to train the model, and then the data samples of model test are selected to simulate it. The model is used to calculate the steel plate surface temperature of the heat treatment furnace. The result indicates that the model is quite simple, and has the strong predictive ability and a broad prospect of applications.
李静;谢挺. 基于LS-SVM的热处理炉钢板温度预报模型[J]. , 2012, 30(8): 41-44.
LI Jing;XIE Ting. Predictive Model of Steel Plate Temperature in Heat Treatment Furnace Based on LS-SVM. , 2012, 30(8): 41-44.
2012, Vol. 30 
