WLAN Indoor GA-ANN Positioning Algorithm via Regularity Encoding Optimization

摘要：

To begin with, for indoor location system, the necessity of research on genetic neural network and its math model are introduced. Then, by analyzing principle of genetic optimized artificial neural network, an indoor location math model of genetic neural network is established. As for various coding types, regularity is taken as the measurement to determine the best coding type for parameter optimization. By analyzing theory of splicing/decomposable coding, the advantages of regularity for such coding type are proved. Finally, through simulation comparisons, to select a regularity coding type for GA-ANN can improve positioning accuracy for indoor environment effectively.

关键词： genetic neural network regularity indoor location splicing/decomposable coding

作者: Lin Ma Ying Sun Mu Zhou Yubin Xu

作者单位: School of Electronics and Information Technology Harbin Institute of Technology Harbin, China

会议类型: 国际会议

会议名称: 2010 International Conference on Communications and Intelligence Information Security(2010年国际信息与智能安全学术会议 ICCIIS2010)

会议地点: 南宁

会议语种:英文

页码: 261-265

在线出版日期: 2010-10-13（万方平台首次上网日期，不代表论文的发表时间）