A neural network approach for simulating stationary stochastic processes

verfasst von

Michael Beer, Pol D. Spanos

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.

Externe Organisation(en)

National University of Singapore
Rice University

Typ

Artikel

Journal

Structural Engineering and Mechanics

Band

32

Seiten

71-94

Anzahl der Seiten

24

ISSN

1225-4568

Publikationsdatum

10.05.2009

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Tief- und Ingenieurbau, Bauwesen, Werkstoffmechanik, Maschinenbau

Elektronische Version(en)

https://doi.org/10.12989/sem.2009.32.1.071 (Zugang: Geschlossen)