A neural network approach for simulating stationary stochastic processes
- authored by
- 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.
- External Organisation(s)
-
National University of Singapore
Rice University
- Type
- Article
- Journal
- Structural Engineering and Mechanics
- Volume
- 32
- Pages
- 71-94
- No. of pages
- 24
- ISSN
- 1225-4568
- Publication date
- 10.05.2009
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Civil and Structural Engineering, Building and Construction, Mechanics of Materials, Mechanical Engineering
- Electronic version(s)
-
https://doi.org/10.12989/sem.2009.32.1.071 (Access:
Closed)