电子发烧友网>电子资料下载>电子论文>机械制造论文>基于粗集理论和支持向量机的动态预测新方法及应用

基于粗集理论和支持向量机的动态预测新方法及应用

2076871 2009-01-07 | rar | 223 | 次下载 | 3积分

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基于粗集的属性约简理论和SVM回归思想，提出了一种内嵌属性约简策略的SVM动态预测方法(RS - SVM)，并用于回转窑烧结带温度测量。该方法首先利用属性约简理论精选出与烧结带温度有重要关联的传感器信号，再利用SVM建立这些传感器信号与烧结带温度之间的非线性映射模型，并不断地跟踪预测误差动态修正SVM预测模型，从而提高了系统的抗干扰性能和容错能力。通过与直接SVM方法进行比较的实验，说明了此方法在回转窑烧结带温度预测的优越性。
关键词粗集理论SVM属性约简动态预测回转窑
Abstract Basedo nth eid eaof th eat tributere ductionof th eor ughse tsth eory( RS)an dth esu pportve ctorm achineer gerssion( SVM)，aki ndof RS -
SVM dynamic prediction approach is presented and applied to predict the temperature of the rotary kiln sintering process. First, the sensor signal that is
closely associated with the sintering temperature are refined场using the attribute reduction theory. Then, a nonlinear imaging model between those sen-
。signals and sintering temperature is established场utilizing SVM，and dynarnically correct the SVM predictive model via continuous tracing the predictive
error, thereby, the anti-interference and the fault-tolerant performances have been improved. Through the comparative experiments between the
direct SVM approach and the RS-SVM approach proposed in this paper, the results show that the RS-SVM approach has superiority in the temperature
predictive task of rotary kiln sintering process.
Keywords Roughs ets SVM Attributesre duction Dynamicp rediction Rotary ki