Robust Extreme Learning Machine With its Application to Indoor Positioning
Robust Extreme Learning Machine With its Application to Indoor Positioning
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DOI：
10.1109/tcyb.2015.2399420
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发表时间：
2016-01
影响因子：
11.8
通讯作者：
Xiaoxuan Lu;Han Zou;Hongming Zhou;Lihua Xie;G. Huang
中科院分区：
文献类型：
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作者：
Xiaoxuan Lu;Han Zou;Hongming Zhou;Lihua Xie;G. Huang
基于位置服务需求的增长促使室内定位系统和室内交互定位系统（IPS）的快速发展。然而，IPS的性能受到噪声测量的影响。在本文中，两种鲁棒的极端学习机（ERM），对应于接近均值约束，和小残差约束，已被提出来解决的问题，在IPS的噪声测量。根据极限学习机中特征映射是否显式，分别给出了基于二阶锥规划的极限学习机的随机隐节点和核化公式。此外，讨论了特征空间中协方差的计算。仿真和室内实验结果表明，与其他基线算法相比，该算法不仅提高了定位精度和重复性，而且减小了定位偏差和最坏情况下的定位误差。
The increasing demands of location-based services have spurred the rapid development of indoor positioning system and indoor localization system interchangeably (IPSs). However, the performance of IPSs suffers from noisy measurements. In this paper, two kinds of robust extreme learning machines (RELMs), corresponding to the close-to-mean constraint, and the small-residual constraint, have been proposed to address the issue of noisy measurements in IPSs. Based on whether the feature mapping in extreme learning machine is explicit, we respectively provide random-hidden-nodes and kernelized formulations of RELMs by second order cone programming. Furthermore, the computation of the covariance in feature space is discussed. Simulations and real-world indoor localization experiments are extensively carried out and the results demonstrate that the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviation and worst case error of IPSs compared with other baseline algorithms.