TY - GEN
T1 - Scaled conjugate gradient neural network for optimizing indoor positioning system
AU - Aburaed, Nour
AU - Atalla, Shadi
AU - Mukhtar, Husameldin
AU - Al-Saad, Mina
AU - Mansoor, Wathiq
PY - 2019/11/21
Y1 - 2019/11/21
N2 - In this paper, several indoor positioning systems are reviewed and a deep neural network (DNN) algorithm based on Scaled Conjugate Gradient (SCG) algorithm is proposed. In the proposed indoor positioning system, Received Signal Strength (RSS) is used as a fingerprint to identify the indoor location in terms of Building and Floor. The performance of the system is evaluated and compared against other machine learning based positioning systems. The accuracy of the proposed DNN is 99% when tested using a standard dataset.
AB - In this paper, several indoor positioning systems are reviewed and a deep neural network (DNN) algorithm based on Scaled Conjugate Gradient (SCG) algorithm is proposed. In the proposed indoor positioning system, Received Signal Strength (RSS) is used as a fingerprint to identify the indoor location in terms of Building and Floor. The performance of the system is evaluated and compared against other machine learning based positioning systems. The accuracy of the proposed DNN is 99% when tested using a standard dataset.
KW - classification
KW - deep neural networks
KW - indoor positioning system
KW - optimization
KW - RSS
KW - scaled conjugate gradient
UR - http://www.scopus.com/inward/record.url?scp=85075930381&partnerID=8YFLogxK
U2 - 10.1109/ISNCC.2019.8909147
DO - 10.1109/ISNCC.2019.8909147
M3 - Conference contribution book
AN - SCOPUS:85075930381
T3 - 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019
BT - 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019
PB - IEEE
CY - Piscataway, N.J.
T2 - 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019
Y2 - 18 June 2019 through 20 June 2019
ER -