基于粗集的屬性約簡理論和SVM回歸思想，提出了一種內(nèi)嵌屬性約簡策略的SVM動態(tài)預測方法(RS - SVM)，并用于回轉(zhuǎn)窯燒結(jié)帶溫度測量。該方法首先利用屬性約簡理論精選出與燒結(jié)帶溫度有重要關(guān)聯(lián)的傳感器信號，再利用SVM建立這些傳感器信號與燒結(jié)帶溫度之間的非線性映射模型，并不斷地跟蹤預測誤差動態(tài)修正SVM預測模型，從而提高了系統(tǒng)的抗干擾性能和容錯能力。通過與直接SVM方法進行比較的實驗，說明了此方法在回轉(zhuǎn)窯燒結(jié)帶溫度預測的優(yōu)越性。
關(guān)鍵詞粗集理論SVM屬性約簡動態(tài)預測回轉(zhuǎn)窯
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場using the attribute reduction theory. Then, a nonlinear imaging model between those sen-
。signals and sintering temperature is established場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