李萬(wàn)慶++左紅++孟文清++陳明欣
摘要: 為客觀、合理地進(jìn)行氣膜薄殼鋼筋混凝土穹頂儲(chǔ)倉(cāng)的工期預(yù)測(cè),提出了基于PSO-SVR的預(yù)測(cè)方法。采用粒子群算法(particle swarm optimization,PSO)對(duì)支持向量回歸機(jī)(support vector regression,SVR)的參數(shù)進(jìn)行優(yōu)化,并運(yùn)用優(yōu)化后的支持向量回歸機(jī)對(duì)氣膜薄殼鋼筋混凝土穹頂儲(chǔ)倉(cāng)的工期進(jìn)行預(yù)測(cè)。通過(guò)實(shí)例驗(yàn)證表明:PSO-SVR模型的預(yù)測(cè)效果優(yōu)于遺傳算法(GA-SVR)和串聯(lián)型灰色神經(jīng)網(wǎng)絡(luò)(SGNN)。
Abstract: In order to forecast the duration on thin-shell concrete dome using inflated forms objectively and reasonably, the artical presents a prediction method named PSO-SVR. Using PSO to optimize the parameter of SVR, and forecasting the duration on thin-shell concrete dome using inflated forms by support vector regression which is optimized. The example show that the prediction effect of PSO-SVR model is better than genetic algorithm (GA-SVR) and series of grey neural network (SGNN).
關(guān)鍵詞: 氣膜薄殼鋼筋混凝土穹頂儲(chǔ)倉(cāng);工期預(yù)測(cè);PSO-SVR
Key words: thin-shell concrete dome using inflated forms;forecasting of the duration;PSO-SVR
中圖分類號(hào):TU722 文獻(xiàn)標(biāo)識(shí)碼:A 文章編號(hào):1006-4311(2017)05-0035-03