Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/54477
Title: A neural network approach for simulating stationary stochastic processes
Authors: Beer, M. 
Spanos, P.D.
Keywords: Monte Carlo simulation
Neural networks
Stochastic processes
Issue Date: 10-May-2009
Source: 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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