Enhancing the Linearity Characteristics of Photoelectric Displacement Sensor Based on Extreme Learning Machine Method

Murugan SETHURAMALINGAM; and Umayal SUBBIAH

doi:10.1007/s13320-014-0219-7

[1] J. Xu, D. Peng, W. Wan, and W. Yang, “New way of producing electrical traveling wave signal based on photo electricity technology in design of time-grating displacement sensor,” Chinese Journal of Sensors and Actuators, 2007, 20(3): 532–535.

[2] T. Xu, H. Lu, and W. Luo, “A robust photoelectric angular position sensor especially for a steerable underground boring tool original,” Sensors and Actuators A: Physical, 2005, 120(2): 311–316.

[3] Y. Yu, M. Ding, and L. Zhang, “The algorithmic analysis of linear approximation for characteristic region of displacement sensor,” Chinese Journal of Sensors and Actuators, 2010, 23(6): 840–843.

[4] Z. Shi, J. Kang, and R. Sun, “BP NN-based method for lens distortion correction of large-field imaging,” Optics and Precision Engineering, 2005, 13(3): 348–353.

[5] W. Ling, Z. Wang, and F. Gao, “Real-time digital correction for distortion in photo electronic measuring system,” Optics and Precision Engineering, 2007, 15(2): 277–282.

[6] Y. Qiao, F. Gao, Z. Wang, Y. Zhao, and J. Li, “Distortion correction for the photoelectricity measuring system based on the cubic fitting equation,” Opto-Electronic Engineering, 2008, 35(6): 28–31.

[7] D. Yu, Z. Su, and K. Yan, “A new type of machine vision systems with algorithm for image correction,” Laser & Infrared, 2008, 38(11): 1173–1176.

[8] X. Bai, S. Cai, F. Gao, Y. Qiao, and M. Dai, “Distortion correction for photo electric measuring system based on BP neural network,” Laser & Infrared, 2010, 40(1): 79–82.

[9] S. Cai, Q. Li, and Y. Qiao, “Camera calibration of attitude measurement system based on BP neural network,” Journal of Optoelectronics Laser, 2007, 18(7): 832–834.

[10] Z. Wang, Y. Li, L. Lou, H. Wei, and Z. Wang, “Application of BP neural network to nonlinearity correction of optical tweezers,” Optics and Precision Engineering, 2008, 16(1): 6–10.

[11] F. X. Zheng and S. P. Ji, “An improved non uniformity correction algorithm for IRFPA based on neural network,” Laser & Infrared, 2008, 38(9): 937–938.

[12] V. Vapnik, Statistical learning theory. New York: John Wiley & Sons Inc., 1998: 12–35.

[13] J. Q. E, Intelligent fault diagnosis and its application. Changsha: Hunan University Press, 2006: 70–130.

[14] A. T. C. Goh and S. H. Goh, “Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data,” Computers and Geotechnics, 2007, 34(5): 410–421.

[15] X. Jiang, Z. Yi, and J. Lv, “Fuzzy SVM with a new fuzzy membership function,” Neural Computing and Application, 2006, 15(2): 268–276.

[16] G. Huang, L. Chen, and C. Siew, “Universal approximation using incremental constructive feed forward networks with random hidden nodes,” IEEE Transactions on Neural Networks, 2006, 17(4): 879–892.

[17] N. Liang, G. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate on-line sequential learning algorithm for feed forward networks,” IEEE Transactions on Neural Networks, 2006, 17(6): 1411–1423.

[18] A. J. Annema, K. Hoen, and H. Wallinga, “Precision requirements for single-layer feed forward neural networks”, In Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, The Netherlands, pp: 145–151, 1994.

[19] G. Huang, Q. Zhu, and C. Siew, “Extreme learning machine: a new learning scheme of feed forward neural networks,” in International Joint Conference on Neural Networks, Singapore, pp: 985–990, 2004.

[20] G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, 2006, 70(1–3): 489–501.