Structural Engineering and Mechanics

  • 제32권1호
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  • Pages.71-94
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  • 2009
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A neural network approach for simulating stationary stochastic processes

  • Beer, Michael (Department of Civil Engineering, National University of Singapore) ;
  • Spanos, Pol D. (Ryon Endowed Chair in Engineering, Rice University)
  • 투고 : 2008.06.11
  • 심사 : 2008.10.08
  • 발행 : 2009.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper a procedure for Monte Carlo simulation of univariate stationary stochastic processes with the aid of neural networks is presented. Neural networks operate model-free and, thus, circumvent the need of specifying a priori statistical properties of the process, as needed traditionally. This is particularly advantageous when only limited data are available. A neural network can capture the "pattern" of a short observed time series. Afterwards, it can directly generate stochastic process realizations which capture the properties of the underlying data. In the present study a simple feed-forward network with focused time-memory is utilized. The proposed procedure is demonstrated by examples of Monte Carlo simulation, by synthesis of future values of an initially short single process record.

키워드

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