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http://dx.doi.org/10.12989/sem.2009.32.1.071

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)
Publication Information
Structural Engineering and Mechanics / v.32, no.1, 2009 , pp. 71-94 More about this Journal
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 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.
Keywords
Monte Carlo simulation; neural networks; stochastic processes;
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