红外与激光工程
紅外與激光工程
홍외여격광공정
INFRARED AND LASER ENGINEERING
2013年
12期
3517-3521
,共5页
极限学习机%光电经纬仪%空间配准%误差修正
極限學習機%光電經緯儀%空間配準%誤差脩正
겁한학습궤%광전경위의%공간배준%오차수정
extreme learning machine%photoelectric theodolite%space registration%error correction
针对光电经纬仪测量中多传感器的空间配准问题,提出了一种基于极限学习机(ELM)的空间配准建模方法。首先介绍了ELM算法和ELM空间配准模型的建立步骤,然后使用星体测量数据建立ELM空间配准模型,最后将该模型与单项差修正模型、球谐函数修正模型进行了对比验证。实验结果表明: ELM空间配准模型可以使光电经纬仪的测量精度从17″左右提高到1″以内,与单项差修正模型、球谐函数修正模型相比精度提高35%以上。由此可见,与单项差修正模型和球谐函数修正模型相比,采用ELM算法所建立的光电经纬仪空间配准模型具有更高的精度和更强的泛化能力。
針對光電經緯儀測量中多傳感器的空間配準問題,提齣瞭一種基于極限學習機(ELM)的空間配準建模方法。首先介紹瞭ELM算法和ELM空間配準模型的建立步驟,然後使用星體測量數據建立ELM空間配準模型,最後將該模型與單項差脩正模型、毬諧函數脩正模型進行瞭對比驗證。實驗結果錶明: ELM空間配準模型可以使光電經緯儀的測量精度從17″左右提高到1″以內,與單項差脩正模型、毬諧函數脩正模型相比精度提高35%以上。由此可見,與單項差脩正模型和毬諧函數脩正模型相比,採用ELM算法所建立的光電經緯儀空間配準模型具有更高的精度和更彊的汎化能力。
침대광전경위의측량중다전감기적공간배준문제,제출료일충기우겁한학습궤(ELM)적공간배준건모방법。수선개소료ELM산법화ELM공간배준모형적건립보취,연후사용성체측량수거건립ELM공간배준모형,최후장해모형여단항차수정모형、구해함수수정모형진행료대비험증。실험결과표명: ELM공간배준모형가이사광전경위의적측량정도종17″좌우제고도1″이내,여단항차수정모형、구해함수수정모형상비정도제고35%이상。유차가견,여단항차수정모형화구해함수수정모형상비,채용ELM산법소건립적광전경위의공간배준모형구유경고적정도화경강적범화능력。
In order to solve the space registration problems of multi-sensor in the photoelectric theodolite measurement, a space registration model based on the extreme learning machine (ELM) algorithm was proposed in this paper. Firstly, the ELM theory and the modeling steps of ELM space registration model were introduced. Then, the star measurement data was used to build ELM space registration model. Finally, the ELM space registration model was compared with single error correction model and spherical harmonics correction model. Experimental results indicate that ELM space registration method can improve the measuring precision of photoelectrical theodolite from about 17″ to less than 1″; the accuracy of the ELM space registration model is improved by more than 35% than single error correction model and spherical harmonics correction model. The results indicate that compare with the single error correction model and spherical harmonics correction model, space registration model based on ELM algorithm has higher prediction accuracy and stronger generalization capability.