Conditional Simulation of No-Gaussian Stochastic Process Based on Neural Networks

Jin Hua Li; Shui Sheng Chen; Wei Bing Sheng

doi:10.4028/www.scientific.net/AMR.479-481.1959

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 479-481Conditional Simulation of No-Gaussian Stochastic...

Conditional Simulation of No-Gaussian Stochastic Process Based on Neural Networks

Abstract:

In many fields such as wind engineering, ocean engineering, soil engineering and so on, it is obvious that the development of effective methods to generate sample functions of non-Gaussian stochastic processes and fields is of paramount significance for many systems subjected to non-Gaussian excitations. In this paper, neural network technique is proposed for the conditional simulation of non-Gaussian stochastic processes and fields. In machine learning of neural network, interpolation is employed to train finite non-Gaussian samples. As numerical examples, the conditional simulation of non-Gaussian fluctuating wind pressures is carried out through using back propagation neural network and generalized regression neural network respectively.

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Periodical:

Advanced Materials Research (Volumes 479-481)

Pages:

1959-1962

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.479-481.1959

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Online since:

February 2012

Authors:

Jin Hua Li, Shui Sheng Chen, Wei Bing Sheng

Keywords:

Back Propagation, Conditional Simulation, Generalized Regression, Neural Network (NN), Non-Gaussian, Stochastic Processes

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References

[1] H. Kameda, and H. Morikawa: Journal of Engineering Mechanics, ASCE 120 (4) (1994) 855-875.

Google Scholar

[2] M. Shinozuka, and R. Zhang: Journal of Engineering Mechanics, ASCE 122 (6) (1996) 530-538.

Google Scholar

[3] H. Kameda, and H. Morikawa: Journal of Engineering Mechanics, ASCE 120 (4) (1993) 855-875.

Google Scholar

[4] K. Gurley, M. Tognarelli, and A. Kareem: Probabilistic Engineering Mechanics 12 (1) (1997) 9-31.

Google Scholar

[5] I. Elishakoff, Y.J. Ren, and M. Shinozuka: Engineering Structures 16 (7) (1994) 558-563.

Google Scholar

[6] K. Gurley, and A. Kareem: Journal of Wind Engineering and Industrial Aerodynamics 77-78 (1998) 39-51.

Google Scholar

[7] R.C. Eberhart, and R.W. Dobbins, in: Neural network PC tools: a practical guide, San Diego, Academic Press, 1990.

Google Scholar

[8] D.F. Specht: IEEE Trans Neural Netw 2 (6) (1991) 568-576.

Google Scholar