Open Science Research Excellence
%0 Journal Article
%A Changqiao Wu and  Guoqing Ding and  Xin Chen
%D 2018 
%J  International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 143, 2018
%T Dynamic Measurement System Modeling with Machine Learning Algorithms
%U http://waset.org/publications/10009825
%V 143
%X 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.
%P 639 - 646