TY - GEN
T1 - Multivariable Support Vector Regression with Multi-sensor Network Data Fusion
AU - Yang, Chan Yun
AU - Lin, Chen Yu
AU - Galsanbadam, Sainzaya
AU - Samani, Hooman
N1 - Funding Information:
ACKNOWLEDGMENT The corresponding author gratefully acknowledges the financial support of the Ministry of Science and Technology of Taiwan through its grants MOST105-2221-E-305-003 and MOST106-2221-E-305-001.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - Motivated by modeling a general behavioral function of a target system, a data-driven multivariable support vector regression (SVR) is developed. The multivariable SVR is sought to estimate a generalized relationship among multiple input variables which would be collected locally from a distributed multi-sensor network. Instead of an immediate estimation with a installed single sensor, the participation of distributed multi sensors gains the estimation of the system states more reliable and more generalized. The proposed SVR modeling method gives rise of the possibility to support multivariable input spectrum, and the flexibility to combine the input variables with respective decay weights. The paper herewith elaborates the development with experimental evidences.
AB - Motivated by modeling a general behavioral function of a target system, a data-driven multivariable support vector regression (SVR) is developed. The multivariable SVR is sought to estimate a generalized relationship among multiple input variables which would be collected locally from a distributed multi-sensor network. Instead of an immediate estimation with a installed single sensor, the participation of distributed multi sensors gains the estimation of the system states more reliable and more generalized. The proposed SVR modeling method gives rise of the possibility to support multivariable input spectrum, and the flexibility to combine the input variables with respective decay weights. The paper herewith elaborates the development with experimental evidences.
KW - multi-sensor network
KW - multiple regression
KW - sensor fusion
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85062206172&partnerID=8YFLogxK
U2 - 10.1109/SMC.2018.00683
DO - 10.1109/SMC.2018.00683
M3 - Conference contribution
AN - SCOPUS:85062206172
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 4029
EP - 4034
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -