|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2011年02 期
| 標(biāo)題 |
| 基于廣義回歸神經(jīng)網(wǎng)絡(luò)的鐵路貨運(yùn)量預(yù)測(33 卷) |
| 英文標(biāo)題 |
| Forecast of Railway Freight Volumes Based on Generalized Regression Neural Network |
| 摘要 |
| 針對(duì)BP神經(jīng)網(wǎng)絡(luò)預(yù)測存在局部極小缺陷和收斂速度慢的問題，提出基于廣義回歸神經(jīng)網(wǎng)絡(luò) (GRNN) 的預(yù)測模型?；谖覈?999—2008年鐵路貨運(yùn)量的歷史統(tǒng)計(jì)數(shù)據(jù)，應(yīng)用GRNN模型和混沌BP神經(jīng)網(wǎng)絡(luò)模型對(duì)鐵路貨運(yùn)量進(jìn)行預(yù)測。通過兩種預(yù)測模型的計(jì)算結(jié)果比較說明，GRNN模型具有良好的收 |
| 作者 |
| 新聞作者:溫愛華，李 松 |
| 關(guān)鍵字 |
