基于極限學(xué)習(xí)機的空間配準(zhǔn)方法
發(fā)布時間：2018-08-09 12:40
【摘要】：對極限學(xué)習(xí)機的特點及適用條件進行了探討,在此基礎(chǔ)上提出和實現(xiàn)了一種基于極限學(xué)習(xí)機的空間配準(zhǔn)方法,并與基于廣義最小二乘和神經(jīng)網(wǎng)絡(luò)的配準(zhǔn)方法在多種場景下進行了仿真比較,結(jié)果驗證了基于極限學(xué)習(xí)機的空間配準(zhǔn)方法的性能優(yōu)勢。
[Abstract]:The characteristics and applicable conditions of extreme learning machines are discussed. On this basis, a spatial registration method based on limit learning machine is proposed and implemented. The simulation is compared with the registration method based on generalized least squares and neural networks in a variety of scenes. The results prove the spatial registration method based on the limit learning machine. Performance advantages.
【作者單位】： 北方自動控制技術(shù)研究所;
【基金】：國家自然科學(xué)基金資助項目(61371064)
【分類號】：E926.4;TP181
本文編號：2174103
[Abstract]:The characteristics and applicable conditions of extreme learning machines are discussed. On this basis, a spatial registration method based on limit learning machine is proposed and implemented. The simulation is compared with the registration method based on generalized least squares and neural networks in a variety of scenes. The results prove the spatial registration method based on the limit learning machine. Performance advantages.
【作者單位】： 北方自動控制技術(shù)研究所;
【基金】：國家自然科學(xué)基金資助項目(61371064)
【分類號】：E926.4;TP181
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