Sensor data fusion by support vector regression methodology - A comparative study

IEEE Sensors Journal

Volumn 15, Issue 2, 2015, Pages 850-854

  Shamshirband, Shahaboddin ^a   Petkovic, Dalibor ^b   Javidnia, Hossein ^c   Gani, Abdullah ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b UNIVERSITY OF NI   (Serbia)

^c UNIVERSITY OF GUILAN   (Iran)

Author keywords

contextual information; Sensor fusion; soft computing; support vector regression

Indexed keywords

FUNCTIONS; INFORMATION FILTERING; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; SOFT COMPUTING;

CONTEXTUAL INFORMATION; GENERALIZATION CAPABILITY; MULTISENSOR DATA FUSION; RADIAL BASIS FUNCTION(RBF); SENSOR FUSION; SOFT COMPUTING METHODOLOGIES; STRONG NONLINEAR SYSTEMS; SUPPORT VECTOR REGRESSION (SVR);

SENSOR DATA FUSION;

EID: 84915746827     PISSN: 1530437X     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSEN.2014.2356501     Document Type: Article

References (23)

1. J. A. Rodger, "A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings," Expert Syst. Appl., vol. 41, no. 4, pp. 1813-1829, 2014
2. M. Grazzini and A. Farina, "Radar fusion to detect targets. Part III," Signal Process., vol. 84, no. 8, pp. 1347-1358, 2004
3. F. Caron, E. Duflos, D. Pomorski, and P. Vanheeghe, "GPS/IMU data fusion using multisensor Kalman filtering: Introduction of contextual aspects," Inf. Fusion, vol. 7, no. 2, pp. 221-230, 2006
4. Z. Lendek, R. Babuška, and B. De Schutter, "Distributed Kalman filtering for cascaded systems," Eng. Appl. Artif. Intell., vol. 21, no. 3, pp. 457-469, 2008
5. C. Y. Dong, Q. Yuan, and Q. Wang, "A combined wavelet analysisfuzzy adaptive algorithm for radar/infrared data fusion," Expert Syst. Appl., vol. 37, no. 3, pp. 2563-2570, 2010
6. J. A. Rodger, "Toward reducing failure risk in an integrated vehicle health maintenance system: A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS," Expert Syst. Appl., vol. 39, no. 10, pp. 9821-9836, 2012
7. C. Mazzà, M. Donati, J. McCamley, P. Picerno, and A. Cappozzo, "An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking," Gait Posture, vol. 35, no. 1, pp. 138-142, 2012
8. H.-X. Zhou, H.-L. Qin, Y.-B. Jian, B.-J. Wang, and S.-Q. Liu, "Improved Kalman-filter nonuniformity correction algorithm for infrared focal plane arrays," Infr. Phys. Technol., vol. 51, no. 6, pp. 528-531, 2008
9. D. Petković, M. Issa, N. D. Pavlović, and L. Zentner, "Intelligent rotational direction control of passive robotic joint with embedded sensors," Expert Syst. Appl., vol. 40, no. 4, pp. 1265-1273, 2013
10. S. Shamshirband, D. Petković, R. Hashim, S. Motamedi, and N. B. Anuar, "An appraisal of wind turbine wake models by adaptive neuro-fuzzy methodology," Int. J. Elect. Power Energy Syst., vol. 63, pp. 618-624, Dec. 2014
11. S. Wu, Y. Wang, and S. Cheng, "Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system," Neurocomputing, vol. 102, pp. 163-175, Feb. 2013
12. S. Shamshirband, D. Petković, R. Hashim, and S. Motamedi, "Adaptive neuro-fuzzy methodology for noise assessment of wind turbine," PLOS ONE, vol. 9, no. 7, p. e103414, 2014
13. D. Petković, N. T. Pavlović, S. Shamshirband, M. L. M. Kiah, N. B. Anuar, and M. Y. IdnaIdris, "Adaptive neuro-fuzzy estimation of optimal lens system parameters," Opt. Lasers Eng., vol. 55, pp. 84-93, Apr. 2014
14. K. Lu, J. Zhao, and D. Cai, "An algorithm for semi-supervised learning in image retrieval," Pattern Recognit., vol. 39, no. 4, pp. 717-720, 2006
15. Y. Leng, X. Xu, and G. Qi, "Combining active learning and semisupervised learning to construct SVM classifier," Knowl.-Based Syst., vol. 44, pp. 121-131, May 2013
16. L. Ornella and E. Tapia, "Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data," Comput. Electron. Agriculture, vol. 74, no. 2, pp. 250-257, 2010
17. P. Jain, J. M. Garibaldi, and J. D. Hirst, "Supervised machine learning algorithms for protein structure classification," Comput. Biol. Chem., vol. 33, no. 3, pp. 216-223, 2009
18. A. Balahura and M. Turchi, "Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis," Comput. Speech Lang., vol. 28, no. 1, pp. 56-75, Jan. 2014
19. S. Chakraborty, "Bayesian semi-supervised learning with support vector machine," Statist. Methodol., vol. 8, no. 1, pp. 68-82, 2011
20. S. Rajasekaran, S. Gayathri, and T.-L. Lee, "Support vector regression methodology for storm surge predictions," Ocean Eng., vol. 35, no. 16, pp. 1578-1587, 2008
21. H. Yang, K. Huang, I. King, and M. R. Lyu, "Localized support vector regression for time series prediction," Neurocomputing, vol. 72, nos. 10-12, pp. 2659-2669, 2009
22. Z. Wei, T. Tao, D. ZhuoShu, and E. Zio, "A dynamic particle filtersupport vector regression method for reliability prediction," Rel. Eng. Syst. Safety, vol. 119, pp. 109-116, Nov. 2013
23. L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. Li, "Iterated time series prediction with multiple support vector regression models," Neurocomputing, vol. 99, pp. 411-422, Jan. 2013.