發(fā)布時間：2018-06-21 09:09

  本文選題：納米鐵粉 + 溫度預(yù)測　； 參考：《中國測試》2017年04期

【摘要】：為提高納米鐵粉的制備工藝,實現(xiàn)納米鐵粉分解爐溫度的精確控制,提出一種基于果蠅優(yōu)化算法和廣義回歸神經(jīng)網(wǎng)絡(luò)的納米鐵粉分解爐溫度預(yù)測方法。該方法采用現(xiàn)場采集數(shù)據(jù),選取進液量和各個溫區(qū)加熱裝置的開度因素來預(yù)測待預(yù)測溫區(qū)溫度。通過廣義回歸神經(jīng)網(wǎng)絡(luò),建立溫度預(yù)測模型,并利用果蠅優(yōu)化算法對光滑因子進行動態(tài)尋優(yōu)。選取不同種群規(guī)模對建立模型進行驗證,并將該文建立模型與普通廣義神經(jīng)網(wǎng)絡(luò)和粒子群算法優(yōu)化的廣義神經(jīng)網(wǎng)絡(luò)模型的預(yù)測效果進行對比。驗證表明:該文建立模型平均相對誤差為0.43%,且能夠排除人為設(shè)置參數(shù)的干擾,具有較好的準確性與穩(wěn)定性,可進一步用于分解爐溫度控制的研究。
[Abstract]:In order to improve the preparation process of nanometer iron powder and realize the accurate control of the temperature of nanometer iron powder decomposing furnace, a temperature prediction method of nanometer iron powder decomposing furnace based on Drosophila optimization algorithm and generalized regression neural network was proposed. In this method, the field data are collected, and the temperature of the temperature region is predicted by selecting the input liquid quantity and the opening factor of the heating device in each temperature zone. The temperature prediction model was established by generalized regression neural network, and the smoothing factor was dynamically optimized by Drosophila optimization algorithm. Different population sizes are selected to verify the model, and the prediction results of this model are compared with that of the generalized neural network model and particle swarm optimization model based on general generalized neural network (GNN) and particle swarm optimization (PSO). The results show that the average relative error of the model is 0.43 and the disturbance of artificial parameters can be eliminated. The model has good accuracy and stability and can be further used in the study of calciner temperature control.
【作者單位】： 長春工業(yè)大學(xué)電氣與電子工程學(xué)院;
【基金】：吉林省重點科技攻關(guān)項目(20140204024GX)
【分類號】：TP18;TB383.1
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本文編號：2048011