Extreme learning machine for wireless indoor localization

IPSN'12 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks

Volumn , Issue , 2012, Pages 101-102

  Xiao, Wendong ^a   Liu, Peidong ^b   Soh, Wee Seng ^b   Jin, Yunye ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

ELM; Fingerprinting; Indoor localization; Neural network

Indexed keywords

ACCESS POINTS; ELM; EXTREME LEARNING MACHINE; FINGERPRINTING; INDOOR LOCALIZATION; LOW COSTS; OFF-LINE LEARNING; ON-LINE LOCALIZATION; RECEIVED SIGNAL STRENGTH; SINGLE LAYER; STORAGE SPACES; TERMINAL DEVICES;

ALGORITHMS; DATA PROCESSING; NEURAL NETWORKS; SENSOR NETWORKS;

LEARNING SYSTEMS;

EID: 84860525092     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2185677.2185697     Document Type: Conference Paper

References (3)

1. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: theory and applications, Neurocomputing 70 (1-3) (2006) 489-501. (Pubitemid 44615772)
2. H. Liu, Houshang Darabi, P. Banerjee, and Jing Liu. Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 37(6):1067-1080, 2007. (Pubitemid 350130704)
3. National Research Council (U.S.). Committee on the Future of the Global Positioning System and National Academy of Public Administration, "The global positioning system: a shared national asset", National Academies Press, Chapter 1, p. 17, 1995.