On the numerical generation of positive-axis-defined distributions with an exponential autocorrelation function
| Autor: | Dima Bykhovsky, Vladimir Lyandres |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Half-normal distribution
Stochastic process Applied Mathematics Autocorrelation Mathematical analysis 020206 networking & telecommunications 02 engineering and technology First order Exponential function Stochastic differential equation 020210 optoelectronics & photonics Computational Theory and Mathematics Artificial Intelligence Signal Processing 0202 electrical engineering electronic engineering information engineering Applied mathematics Computer Vision and Pattern Recognition Electrical and Electronic Engineering Statistics Probability and Uncertainty Mathematics |
| Zdroj: | Digital Signal Processing. 77:43-47 |
| ISSN: | 1051-2004 |
| DOI: | 10.1016/j.dsp.2017.07.012 |
| Popis: | Stochastic modeling commonly requires random process generation with an exponential autocorrelation function (ACF). These random processes may be represented as a solution of a stochastic differential equation (SDE) of the first order and usually have one-sided (positive-axis-defined) distributions. However, adoption of the SDE-based method faces serious limitations due to difficulties with the numerical solution. To overcome this issue we propose a tractable general numerical solution of the above-mentioned SDE that preserves solution positivity and accuracy, and validate it with numerical simulations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect