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|Title:||A neural network approach for simulating stationary stochastic processes||Authors:||Beer, M.
|Keywords:||Monte Carlo simulation
|Issue Date:||10-May-2009||Citation:||Beer, M.,Spanos, P.D. (2009-05-10). A neural network approach for simulating stationary stochastic processes. Structural Engineering and Mechanics 32 (1) : 71-94. ScholarBank@NUS Repository.||Abstract:||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.||Source Title:||Structural Engineering and Mechanics||URI:||http://scholarbank.nus.edu.sg/handle/10635/54477||ISSN:||12254568|
|Appears in Collections:||Staff Publications|
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