Meshless weighted least - square method 加權(quán)最小二乘無(wú)網(wǎng)格法
Forecasting water yield of mine with the partial least - square method and neural network 偏最小二乘回歸神經(jīng)網(wǎng)絡(luò)的礦坑涌水量預(yù)測(cè)
Least - square method 最小二乘法
To date only discrete points are obtained , but not the surface of the scene . hence a least - square method is used to fit terrain surface 對(duì)整幅圖來(lái)說(shuō),完成點(diǎn)的重建后得到的僅僅是圖像上的一些離散點(diǎn)。
When the data is not in normal or logarithmic normal distribution , the least - squared method is not applicable to obtaining the sensitive index 若試驗(yàn)數(shù)據(jù)為非正態(tài)或非對(duì)數(shù)正態(tài)分布,最小二乘法求優(yōu)會(huì)受到限制。
Having obtained an initial position with a least - square method , a more precise result can be worked out from information contained in the bearing measurements 在利用最小二乘法獲得定位初值后,根據(jù)每次角度測(cè)量的信息進(jìn)行目標(biāo)位置估計(jì)。
Comparison of the estimator from the least - squares method with the robust method shows that the estimator from the robust estimator is more reliable 通過(guò)比較,對(duì)于各測(cè)線上所有采樣點(diǎn)的系統(tǒng)誤差補(bǔ)償來(lái)說(shuō),系統(tǒng)誤差模型的抗差估計(jì)比最小二乘估計(jì)的可靠性更高。
This paper elaborates on principles and calculation methods of experiment of prestress tube frictional resistance and also makes data procession of the experimental results by using least - square method 摘要詳細(xì)闡述了預(yù)應(yīng)力束管道摩阻試驗(yàn)的原理及計(jì)算方法,采用最小二乘法對(duì)試驗(yàn)結(jié)果進(jìn)行數(shù)據(jù)處理。
In this paper , we used weighted neural network and least - square method for filling out the missed data , and compared the result obtained respectively with that of the neural network and the least - square method 運(yùn)用神經(jīng)網(wǎng)絡(luò)和最小二乘法加權(quán)的方法對(duì)缺損數(shù)據(jù)進(jìn)行填充,并分別與最小二乘法和神經(jīng)網(wǎng)絡(luò)的處理結(jié)果進(jìn)行比較。
Structure risk minimization based weighted partial least - squared method weighted partial least - squared wpls method was proposed to achieve structure risk minimization in the partial least - squares modeling process 為了在偏最小二乘法pls建模過(guò)程中實(shí)現(xiàn)結(jié)構(gòu)風(fēng)險(xiǎn)最小化srm ,提出基于結(jié)構(gòu)風(fēng)險(xiǎn)最小化的加權(quán)偏最小二乘法wpls 。