【摘要】：真空玻璃傳熱過(guò)程是非線性復(fù)雜的系統(tǒng)。為了研究真空玻璃的保溫性能,提出一種基于模糊信息�；蚅SSVM真空玻璃保溫性能預(yù)測(cè)研究的智能檢測(cè)方法。根據(jù)工業(yè)現(xiàn)場(chǎng)采集數(shù)據(jù),考慮真空玻璃傳熱過(guò)程的選擇透過(guò)性,將采集的多元樣本數(shù)據(jù)進(jìn)行模糊粒化處理,提取各窗口有效的分量信息,建立基于最小二乘支持向量機(jī)的真空玻璃保溫性能的預(yù)測(cè)模型,實(shí)現(xiàn)對(duì)真空玻璃非熱源一側(cè)溫度平均值和波動(dòng)范圍的聯(lián)合預(yù)測(cè)。利用自適應(yīng)模糊粒子群算法進(jìn)行迭代,獲取更優(yōu)的模型參數(shù),提高模型的性能。研究結(jié)果表明:預(yù)測(cè)結(jié)果在0℃~0.5℃,在一定波動(dòng)范圍內(nèi),能夠有效預(yù)測(cè)真空玻璃的保溫性能。
[Abstract]:The heat transfer process of vacuum glass is a nonlinear and complex system. In order to study the thermal insulation performance of vacuum glass, an intelligent testing method based on fuzzy information granulation and LSSVM vacuum glass thermal insulation performance prediction was proposed. According to the data collected in the industrial field and considering the selectivity and permeability of the heat transfer process of vacuum glass, the collected multivariate sample data are processed by fuzzy granulation, and the effective component information of each window is extracted. Based on least square support vector machine (LS-SVM), a prediction model of vacuum glass thermal insulation performance is established, and the joint prediction of the average temperature and fluctuation range on one side of the vacuum glass non-heat source is realized. The adaptive fuzzy particle swarm optimization algorithm is used to iterate to obtain better model parameters and improve the performance of the model. The results show that the thermal insulation properties of vacuum glass can be effectively predicted by the predicted results at 0 鈩，

本文編號(hào)：2189529