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RISS 인기검색어
- 검색키워드
- 저자 : Pol D. Spanos (검색결과 2 건)
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A neural network approach for simulating stationary stochastic processes
Beer, Michael,Spanos, Pol D. Techno-Press 2009 Structural Engineering and Mechanics, An Int'l Jou Vol.32 No.1
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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A neural network approach for simulating stationary stochastic processes
Michael Beer,Pol D. Spanos 국제구조공학회 2009 Structural Engineering and Mechanics, An Int'l Jou Vol.32 No.1
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 feedforward 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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