Pregled bibliografske jedinice broj: 202782
WLAN Location Determination Model Based on the Artificial Neural Networks
WLAN Location Determination Model Based on the Artificial Neural Networks // PROCEEDINGS ELMAR-2005 / Grgić, Mislav ; Kos, Tomislav ; Grgić, Sonja (ur.).
Zagreb: Hrvatsko društvo Elektronika u pomorstvu (ELMAR), 2005. str. 287-290 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
WLAN Location Determination Model Based on the Artificial Neural Networks
Autori
Vilović, Ivan ; Zovko-Cihlar, Branka
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
PROCEEDINGS ELMAR-2005
/ Grgić, Mislav ; Kos, Tomislav ; Grgić, Sonja - Zagreb : Hrvatsko društvo Elektronika u pomorstvu (ELMAR), 2005, 287-290
Skup
47th International Symposium ELMAR-2005 focused on Multimedia Systems and Applications
Mjesto i datum
Zadar, Hrvatska, 08.06.2005. - 10.06.2005
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
WLAN; Artificial neural network; Location determination; Synaptic weights; Activation function; Back-propagation algorithm; Conjugate gradient algorithm
Sažetak
Along with increasing popularity of wireless LAN, problem of location determination for mobile users becomes more important. The strengths of RF signals arriving from several access points can be used for location determination of the mobile terminal. In indoor environments the received signal level is very complex function of the distance. The solution can be found in the area of artificial neural networks. The neural networks can be learned to classify data. Labeled data examples of signal strengths at known locations must be collected by the measurement. This data will serve for the training of the network with appropriate training algorithm. The trained network is capable to determine location on the base of new signal strengths as a process of generalization. The advantage of the method is that it doesn't need any extra hardware, while with flexible neural network model achieves lower distance errors in determining position comparable with other methods. For successful position determination only what is needed are a map of indoor space and several identified locations to train the network.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Projekti:
0036015
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Sveučilište u Dubrovniku