rmse root mean square error 中文意思是什麼

rmse root mean square error 解釋
均方根誤差
  • rmse : 均方根誤差
  • root : n 魯特〈姓氏〉。n 1 (草木、毛發等的)根;根菜,食用菜根;根莖,地下莖;塊根;有根植物,草木,草...
  • mean : vt 1 意,有…的意思,意思是…。2 意指,用…意思說;意味著,就是。3 (用語言、繪畫等)表示意思,表示...
  • square : n 1 正方形,四方塊,四角;方形物。2 (方形)廣場;〈美國〉(四面都是馬路的)方陣建築;街區;(方...
  • error : n. 1. 錯誤;失錯。2. 謬見,誤想;誤信;誤解。3. 罪過。4. 【數學】誤差;【法律】誤審,違法;(棒球中的)錯打。adj. -less 無錯誤的,正確的。
  1. During the course of the research, the criterions of the interpolation effect are mean error ( me ), mean absolute error ( mae ), root mean squared interpolation error ( rmse ) and the difference of mean square deviation between the measured and the estimated surface air temperature. the conclusions are as follows : ( 1 ) by contrasting the gaussian weighted model associated with the error modification with the gaussian weighted model, the error modification is proved to considerably ameliorate the precision of spatial interpolation ; ( 2 ) on the base of the gaussian weighted model, taking altitudinal effect into account can reflect the trend in which temperature changes according to the topographic altitude and may ameliorate the precision of spatial interpolation correspondingly and apparently, which indicates that topographical effect on the preciseness of spatial interpolation can not be disregarded in terms of the region with complicated topography ; ( 3 ) the map of daily surface air temperature distribution, using the modified gaussian weighted model a and b, can accurately reflect the temperature - changing - with - topographical - altitude trend. among them, the better is the model a, whose me is below 0. 03 ?

    在此過程中,採用平均誤差( me ) ,平均絕對誤差( mae ) ,插值平均誤差平方的平方根( rootmeansquaredinterpolationerror ,簡稱rmsie ) ,插值前後測站要素值的均方差( meansquaredeviation ,簡稱msd )差值作為判定插值效果的標準,得出如下結論:通過高斯權重法與結合逐步訂正的高斯權重法的對比,說明結合逐步訂正方案的高斯權重法可大大提高地面日氣溫的插值精度;在高斯權重法中加入海拔影響項可以反映出溫度隨地形高度的變化趨勢,同時也能較大地提高地面日氣溫的空間插值精度,說明在地形復雜的區域,地形影響在插值精度中是不可忽略的;對于高斯權重法的兩種改進方案得到的地面日氣溫分布圖都能很好地反映出表面大氣氣溫隨地形高度的變化趨勢。
  2. Results show that the rbfnn is obviously superior to the traditional linear model, and its mae ( mean absolute error ) and rmse ( root mean square error ) are 41. 8 and 55. 7, respectively

    結果顯示,該模型預測效果明顯優于傳統的線性自回歸預測模型,各月平均的平均絕對誤差( mae )和均方誤差( rmse )達到41 . 8和55 . 7 。
  3. When feature point sets are extracted respectively from the two images, correspondence between the point sets is then established by a two - stage matching algorithm. this matching algorithm is based on the alignment metric and < wp = 4 > rmse ( root mean square error )

    對兩幅圖像分別提取廣義特徵點集之後,提出一種基於對齊度準則和根均方誤差的兩步匹配演算法完成同名控制點的建立。
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