Dynamic Measurement System Modeling with Machine Learning Algorithms
In this paper, ways of modeling dynamic measurement
systems are discussed. Specially, for linear system with single-input
single-output, it could be modeled with shallow neural network.
Then, gradient based optimization algorithms are used for searching
the proper coefficients. Besides, method with normal equation and
second order gradient descent are proposed to accelerate the modeling
process, and ways of better gradient estimation are discussed. It
shows that the mathematical essence of the learning objective is
maximum likelihood with noises under Gaussian distribution. For
conventional gradient descent, the mini-batch learning and gradient
with momentum contribute to faster convergence and enhance model
ability. Lastly, experimental results proved the effectiveness of second
order gradient descent algorithm, and indicated that optimization with
normal equation was the most suitable for linear dynamic models.
[1] J. P. Xiang, “Dynamic properties of force transducer,” Process
Automation instrumentation, no. 6, pp. 12–21+102, 1981.
[2] A. Simpkins, “System identification: Theory for the user, 2nd edition,”
IEEE Robotics Automation Magazine, vol. 19, no. 2, pp. 95–96, June
2012.
[3] K. J. Xu and M. Yin, “A dynamic modeling method based on flann for
wrist force sensor,” Chinese Journal of Scientific Instrument, vol. 21,
no. 1, pp. 92–94, 2000.
[4] S. P. Tian, P. P. Jiang, and G. Z. Yan, “Application o-f recurrent neural
network to dynamic modeling of sensors,” Chinese Journal of Scientific
Instrument, vol. 25, no. 5, pp. 574–576, 2004. [5] X. D. Wang, C. J. Zhang, and H. R. Zhang, “Sensor dynamic
modeling using least square support vector machines,” Chinese Journal
of Scientific Instrument, vol. 27, no. 7, pp. 730–733, 2006.
[6] D. H. Wu, W. Zhao, S. L. Huang, and S. K. He, “Research on improved
flann for sensor dynamic modeling,” Chinese Journal of Scientific
Instrument, no. 2, pp. 362–367, 2009.
[7] W. J. Yang, “Research on dynamic characteristics and compensation
technology of pressure sensors,” Master’s thesis, North University of
China, Shanxi, 2017.
[8] T. Dab´oczi, “Uncertainty of signal reconstruction in the case of jittery
and noisy measurements,” IEEE Transactions on Instrumentation and
Measurement, vol. 47, no. 5, pp. 1062–1066, 1998.
[9] J. Brignell, “Software techniques for sensor compensation,” Sensors and
Actuators A: Physical, vol. 25, no. 1-3, pp. 29–35, 1990.
[10] M. A. Nielsen, Neural Networks and Deep Learning. Determination
Press, 2015.
[11] S. P. Tian, Y. Zhao, W. H. Yu, and Z. W. Wang, “Nonlinear
compensation of sensors based on bp neural network,” Journal of Test
and Measurement Technology, vol. 21, no. 1, pp. 84–89, 2007.
[12] She Ping Tian, “Nonlinear dynamic compensation of sensors based
on recurrent neural network model,” Journal of Shanghai Jiao Tong
University, vol. 37, no. 1, pp. 13–16, 2003.
[13] H. M. Huang, “Dynamical compensation method for weighting sensor
based on flann,” Transducer and Microsystem Technologies, vol. 25,
no. 8, pp. 25–28, 2006.
[14] L. Q. Hou, W. G. Tong, and T. X. He, “Nonlinear errors correcting
method of sensors based on rbf neural network,” Journal of Transducer
Technology, vol. 17, no. 4, pp. 643–646, 2004.
[15] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard,
W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten
zip code recognition,” Neural computation, vol. 1, no. 4, pp. 541–551,
1989.
[16] H. Li, Statistical learning method. Beijing: Tsing Hua University Press,
2012.
[17] D. H.Wu, “Dynamic compensating method for transducer based on flann
inverse system constructed by ls-svm,” Journal of Data Acquisition and
Processing, vol. 22, no. 3, pp. 378–383, 2007.
[18] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press,
2016.
[19] N. Qian, “On the momentum term in gradient descent learning
algorithms,” Neural Networks, vol. 12, no. 1, pp. 145–151, 1 1999.
[1] J. P. Xiang, “Dynamic properties of force transducer,” Process
Automation instrumentation, no. 6, pp. 12–21+102, 1981.
[2] A. Simpkins, “System identification: Theory for the user, 2nd edition,”
IEEE Robotics Automation Magazine, vol. 19, no. 2, pp. 95–96, June
2012.
[3] K. J. Xu and M. Yin, “A dynamic modeling method based on flann for
wrist force sensor,” Chinese Journal of Scientific Instrument, vol. 21,
no. 1, pp. 92–94, 2000.
[4] S. P. Tian, P. P. Jiang, and G. Z. Yan, “Application o-f recurrent neural
network to dynamic modeling of sensors,” Chinese Journal of Scientific
Instrument, vol. 25, no. 5, pp. 574–576, 2004. [5] X. D. Wang, C. J. Zhang, and H. R. Zhang, “Sensor dynamic
modeling using least square support vector machines,” Chinese Journal
of Scientific Instrument, vol. 27, no. 7, pp. 730–733, 2006.
[6] D. H. Wu, W. Zhao, S. L. Huang, and S. K. He, “Research on improved
flann for sensor dynamic modeling,” Chinese Journal of Scientific
Instrument, no. 2, pp. 362–367, 2009.
[7] W. J. Yang, “Research on dynamic characteristics and compensation
technology of pressure sensors,” Master’s thesis, North University of
China, Shanxi, 2017.
[8] T. Dab´oczi, “Uncertainty of signal reconstruction in the case of jittery
and noisy measurements,” IEEE Transactions on Instrumentation and
Measurement, vol. 47, no. 5, pp. 1062–1066, 1998.
[9] J. Brignell, “Software techniques for sensor compensation,” Sensors and
Actuators A: Physical, vol. 25, no. 1-3, pp. 29–35, 1990.
[10] M. A. Nielsen, Neural Networks and Deep Learning. Determination
Press, 2015.
[11] S. P. Tian, Y. Zhao, W. H. Yu, and Z. W. Wang, “Nonlinear
compensation of sensors based on bp neural network,” Journal of Test
and Measurement Technology, vol. 21, no. 1, pp. 84–89, 2007.
[12] She Ping Tian, “Nonlinear dynamic compensation of sensors based
on recurrent neural network model,” Journal of Shanghai Jiao Tong
University, vol. 37, no. 1, pp. 13–16, 2003.
[13] H. M. Huang, “Dynamical compensation method for weighting sensor
based on flann,” Transducer and Microsystem Technologies, vol. 25,
no. 8, pp. 25–28, 2006.
[14] L. Q. Hou, W. G. Tong, and T. X. He, “Nonlinear errors correcting
method of sensors based on rbf neural network,” Journal of Transducer
Technology, vol. 17, no. 4, pp. 643–646, 2004.
[15] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard,
W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten
zip code recognition,” Neural computation, vol. 1, no. 4, pp. 541–551,
1989.
[16] H. Li, Statistical learning method. Beijing: Tsing Hua University Press,
2012.
[17] D. H.Wu, “Dynamic compensating method for transducer based on flann
inverse system constructed by ls-svm,” Journal of Data Acquisition and
Processing, vol. 22, no. 3, pp. 378–383, 2007.
[18] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press,
2016.
[19] N. Qian, “On the momentum term in gradient descent learning
algorithms,” Neural Networks, vol. 12, no. 1, pp. 145–151, 1 1999.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:78225", author = "Changqiao Wu and Guoqing Ding and Xin Chen", title = "Dynamic Measurement System Modeling with Machine Learning Algorithms", abstract = "In this paper, ways of modeling dynamic measurement
systems are discussed. Specially, for linear system with single-input
single-output, it could be modeled with shallow neural network.
Then, gradient based optimization algorithms are used for searching
the proper coefficients. Besides, method with normal equation and
second order gradient descent are proposed to accelerate the modeling
process, and ways of better gradient estimation are discussed. It
shows that the mathematical essence of the learning objective is
maximum likelihood with noises under Gaussian distribution. For
conventional gradient descent, the mini-batch learning and gradient
with momentum contribute to faster convergence and enhance model
ability. Lastly, experimental results proved the effectiveness of second
order gradient descent algorithm, and indicated that optimization with
normal equation was the most suitable for linear dynamic models.", keywords = "Dynamic system modeling, neural network, normal
equation, second order gradient descent.", volume = "12", number = "11", pages = "639-8", }
