Location determination of mobile devices for an indoor WLAN application using a neural network

Knowledge and Information Systems

Volumn 20, Issue 1, 2009, Pages 81-93

  Tsai, Chih Yung ^a   Chou, Shuo Yan ^b   Lin, Shih Wei ^c   Wang, Wei Hao ^b  

^a NATIONAL PENGHU UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^c CHANG GUNG UNIVERSITY   (Taiwan)

Author keywords

Back propagation network; Location determination; Mobile devices; Wireless LAN

Indexed keywords

ADAPTIVE ALGORITHMS; BACKPROPAGATION; LOCATION; MOBILE COMPUTING; MUSEUMS; NEURAL NETWORKS; TELECOMMUNICATION SERVICES; WIRELESS LOCAL AREA NETWORKS (WLAN);

ACCESS POINT (APS); BACK-PROPAGATION NEURAL NETWORKS; BACKPROPAGATION NETWORK; COMBINED STRENGTHS; INDOOR ENVIRONMENT; LOCATION DETERMINATION; OUTDOOR ENVIRONMENT; WLAN APPLICATIONS;

LOCATION BASED SERVICES;

EID: 67650003712     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-008-0154-2     Document Type: Article

References (34)

1. Abe N, Kudo M (2005) Non-parametric classifier-independent feature selection. Pattern Recognit 39(5):737-746
2. Castillo PA, Carpio J, Merelo JJ, Prieto A, Rivas V, Romero G (2000) Evolving multilayer perceptrons. Neural Process Lett 12:115-127
3. Fujita LM (1994) Neural networks in computer intelligence. McGraw-Hill, New York
4. Geok SN, Khue HC, Sevki SE, Harcharan S (2000) Neural network learning using entropy cycle. Knowl Inf Syst 2:53-72
5. Ghosh R, Verma B (2003) A hierarchical method for finding optimal architecture and weights using evolutionary least square based learning. Int J Neural Syst 13:13-24
6. Gupta JND, Sexton RS (1999) Comparing backpropagation with a genetic algorithm for neural network training. Int J Manag Sci 27:679-684
7. Gupta M, Tu M, Khan L, Bastani F, Yen I-L (2005) A study of the model and algorithms for handling location-dependent continuous queries. Knowl Inf Syst 8(4):414-437
8. Kaemarungsi K (2006) Distribution of WLAN received Signal strength indication for indoor location determination, In: Proceeding of first international symposium on wireless pervasive computing, pp 1-6
9. Karl H, Willig A (2005) Protocols and architectures for wireless sensor networks. Wiley, New York
10. Khaw JFC, Lim BS, Lim LEN (1995) Optimal design of neural networks using the Taguchi method. Neurocomputing 7:225-245
11. Kim KJ, Han I (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19:125-132
12. Lezoray O, Cardot H (2001) A neural network architecture for data classification. Int J Neural Syst 11:33-42
13. Lin S-W, Tseng T-Y, Chou S-Y, Chen S-C (2008) A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks. Expert Syst Appl 34:1491-1499
14. Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining. Kluwer, Boston
15. Patwari N, Ash JN, Kyperountas S, Hero AO, Moses RL, Correal NS (2005) Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Process Mag 22(4):54-69
16. Reed R (1993) Pruning algorithms: A survey. IEEE Trans Neural Netw 4(5):740-747
17. Romeijn HE, Smith RL (1994) Simulated annealing constrained global optimization. J Global Optim 5:101-126
18. Romeijn HE, Zabinsky ZB, Graesser DL, Neogi S (1999) New reflection generator for simulated annealing in mixed-integer/continuous global optimization. J Optim Theory Appl 101:403-427
19. Scott J, Hazas M (2003) User-friendly surveying techniques for location-aware systems. In: Proceedings of the international conference on ubiquitous computing, pp 45-54
20. Sexton RS, Alidaee B, Dorsey RE, Johnson JD (1998) Global optimization for artificial neural networks: A tabu search application. Eur J Oper Res 106:570-584
21. Sexton RS, Dorsey RE, Johnson JD (1999) Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. Eur J Oper Res 114:589-601
22. Sexton RS, Mcmurtrey S, Cleavenger DJ (2006) Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem. Eur J Oper Res 168:1009-1018
23. Sheila G, Stefan W (2006) Call classification using recurrent neural networks, support vector machines and finite state automata. Knowl Inf Syst 9(2):131-156
24. Sietsma J, Dow R (1988) Neural net pruning: Why and how. In: Proceedings of the IEEE international conference on neural networks, pp 325-333
25. Sivagaminathan R, Ramakrishnan KS (2006) A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert Syst Appl 31(1):49-60
26. Stella M, Russo M, Begusic D (2007) Location determination in indoor environment based on RSS fingerprinting and artificial neural network, In: Proceedings of the 9th international conference on telecommunications, pp 301-305
27. Verikas A, Bacauskiene VM (2002) Feature selection with neural networks. Pattern Recognit Lett 23:1323-1335
28. Verma B (1997) Fast training of multilayer perceptrons. IEEE Trans Neural Netw 8(6):1314-1320
29. Vilovic I, Zovko-Cihlar B (2005) WLAN location determination model based on the artificial neural networks, In: Proceedings of the 47th international symposium ELMAR, pp 287-290
30. Widrow B (1998) Layered neural nets for pattern recognition. IEEE Trans Acoust Speech Signal Process 36(7):1109-1117
31. Yamazaki D, Souto MC, Luderimir P (2002) Optimization of neural network weights and architectures for outdoor recognition using simulated annealing. In: Proceedings of IEEE on neural networks, pp 547-552
32. Yeung S, Zeng XQ (2002) Hidden neuron pruning for multilayer perceptrons using a sensitivity measure. In: Proceedings of IEEE on machine learning and cybernetics, pp 1751-1757
33. Zhang L, Jack LB, Nandi AK (2005) Fault detection using genetic programming. Mech Syst Signal Process 19:271-289
34. Zhou ZH, Chen SF, Chen Z-Q (2000) FANNC: A fast adaptive neural network classifier. Knowl Inf Syst 2(1):115-129