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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 테크노프레스 Structural engineering and mechanics : An international journal Structural engineering and mechanics : An international journal 제32권 제1호
- 발행연도
- 2009.1
- 수록면
- 71 - 94 (24page)
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초록· 키워드
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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.
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