在實(shí)際應(yīng)用中，需要不斷將新測(cè)的監(jiān)測(cè)數(shù)據(jù)模式補(bǔ)充到訓(xùn)練模式集中，以保持模式識(shí)別器的時(shí)效性。現(xiàn)實(shí)計(jì)算條件要求訓(xùn)練模式數(shù)保持一個(gè)適當(dāng)?shù)囊?guī)模，因此在增加一些訓(xùn)練模式時(shí)要淘汰相應(yīng)數(shù)量的最“老”的訓(xùn)練模式。
5．實(shí)例分析
作為應(yīng)用實(shí)例，以小浪底水利樞紐出水口高邊坡某斷面一段時(shí)間的監(jiān)測(cè)數(shù)據(jù)進(jìn)行分析。
表1列出了進(jìn)行監(jiān)測(cè)資料分析試驗(yàn)的監(jiān)測(cè)時(shí)刻，它們對(duì)應(yīng)的監(jiān)測(cè)數(shù)據(jù)向量模式列于表2。分析時(shí)只需從小浪底水利樞紐安全監(jiān)控系統(tǒng)的原始數(shù)據(jù)庫(kù)中調(diào)出這些數(shù)據(jù)即可。
表1 小浪底水利樞紐出水口高邊坡某斷面監(jiān)測(cè)資料分析模式識(shí)別采樣情況
|
1 |
2000-1-3 14:18 |
|
2 |
2000-1-10 08:24 |
|
3 |
2000-1-17 15:40 |
|
4 |
2000-1-24 08:45 |
|
5 |
2000-1-31 15:00 |
|
6 |
2000-2-7 14:55 |
V1=(16.5, 13.95,8.25,2.85,1.45,0.65,13.35, 13.15, 8.25, 1.65, 2.45, 2.25, 1876.2, 1815.51, 1403.69, 1687.55, 1682.21, 1822.02, -19.82, -38.61, 0, 0, 0, 0)
V2=(16.75,13.8, 8.2, 2.75,1.4, 0.6, 13.3, 13, 8.2, 1.6, 2.4, 2.25, 1875.66, 1817.09, 1397.67, 1686.91, 1694.52, 1822.15, -19.29, -4.21, 0, 0, 0, 0)
V3=(16.95,12.95,8.3, 2.9, 1.35,0.6, 13.25, 12.95, 8.15, 1.6, 2.35, 2.2, 1872.03, 1817.2, 1403.9, 1686.03, 1693.11, 1822.45, -18.75, -4.04, 0, 0, 0, 0)
V4=(15.8, 12.8, 8.2, 3.05,1.5, 0.65,13.35, 13.05, 8.2, 1.65, 2.5, 2.25, 1873.69, 1816.26, 1402.31, 1681.57, 1681.91, 1822.96, -17.33, -4.04, 0, 0, 0, 0)
V5=(15.75,13.15,8.2, 3.03,1.5, 0.6, 13.3, 13.25, 8.15, 1.6, 2.4, 2.2, 1874.54, 1816.32, 1409, 1686.83, 1662.81, 1826.46, -18.93, -6.44, 0, 0, 0, 0)
V6=(15.85,13.25,8.15,2.9, 1.4, 0.6, 13.35, 13.35, 8.2, 1.7, 2.4, 2.25, 1874.68, 1816.27, 1405.01, 1687.63, 1701.47, 1825.66, -19.91, -5.19, 0, 0, 0, 0)
將上述監(jiān)測(cè)數(shù)據(jù)向量輸入邊坡監(jiān)測(cè)模式識(shí)別器，即刻可求出對(duì)應(yīng)的邊坡滑動(dòng)模式，如表2所示。
這些分析成果可成為邊坡極限分析程序數(shù)據(jù)文件的基礎(chǔ)，應(yīng)用于邊坡的穩(wěn)定分析中，也可以用來(lái)推測(cè)邊坡穩(wěn)定的主要因素。
6．結(jié)語(yǔ)
結(jié)合自動(dòng)監(jiān)測(cè)儀器系統(tǒng)的使用，應(yīng)用人工神經(jīng)網(wǎng)絡(luò)模式識(shí)別技術(shù)和邊坡極限分析理論，可實(shí)現(xiàn)邊坡安全監(jiān)測(cè)資料分析的自動(dòng)化。自動(dòng)化的在線(xiàn)監(jiān)測(cè)功能和準(zhǔn)確的分析成果將顯著提高水電工程管理部門(mén)對(duì)邊坡安全和整個(gè)水電工程系統(tǒng)運(yùn)行的可靠性的管理水平。
表2 小浪底水利樞紐出水口高邊坡某斷面監(jiān)測(cè)資料模式識(shí)別分析情況
|
序號(hào) |
時(shí)間 |
滑裂面型式 |
|
1 |
2000-1-3 14:18 |
1 |
|
2 |
2000-1-10 08:24 |
1 |
|
3 |
2000-1-17 15:40 |
1 |
|
4 |
2000-1-24 08:45 |
1 |
|
5 |
2000-1-31 15:00 |
1 |
|
6 |
2000-2-7 14:55 |
3 |
參考文獻(xiàn)：
陸峰，博士學(xué)位論文《邊坡監(jiān)測(cè)的模式識(shí)別和極限分析研究》，中國(guó)水利水電科學(xué)研究院，2001.8
陳祖煜，《巖質(zhì)高邊坡穩(wěn)定分析和軟件系統(tǒng)》，中國(guó)水利水電科學(xué)研究院，1995.5
Abhijit S. Pandya, Robert B. Macy 著, 徐勇, 荊濤譯, 神經(jīng)網(wǎng)絡(luò)模式識(shí)別及其實(shí)現(xiàn), 電子工業(yè)出版社, 1999.6
戴葵. 神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)技術(shù). 國(guó)防科技大學(xué)出版社, 1998.7
Pattern Recognition Method in Slope Monitor
Abstract: A concept of slice patterns is put forward in this paper. By using slice patterns in slope monitor, we can build the mapping relations between slice patterns and monitor information. Introducing the pattern recognition method of Artificial Neural Network, a pattern recognizer for slope monitor is built to judge the safe state patterns of the slopes at any monitor times based on the slope monitor information. The stability of slopes can be estimated based on the above information. As a case, this pattern recognizer is applied in analyzing a section of monitor data of Xiaolangdi Outtake Slope. It is showed that the effects of this pattern recognizer is reliable enough for slope monitor.
Keyword: Slope; Safe Monitor; Pattern Recognition; Artificial Neural Network
