Stochastic dynamical systems often contain non-linearities that make it hard to compute probability density functions or statistical moments of these systems. For the moment computations, nonlinearities lead to the well-known problem of unclosed moment dynamics, i.e., differential equations that govern the time evolution of moments up to a certain order may contain some moments of higher order. Moment closure techniques are used to find an approximate, closed system of equations for the moment dynamics, but their usage is rather limited for systems with continuous states particularly when the nonlinearities are non-polynomials. Here, we extend a moment closure technique based on derivative matching, which was originally proposed for polynomial stochastic systems with discrete states, to continuous state stochastic differential equations with both polynomial and trigonometric nonlinearities.
|Original language||English (US)|
|Title of host publication||2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jan 18 2018|
|Event||56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia|
Duration: Dec 12 2017 → Dec 15 2017
|Name||2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017|
|Other||56th IEEE Annual Conference on Decision and Control, CDC 2017|
|Period||12/12/17 → 12/15/17|
Bibliographical noteFunding Information:
AS was supported by the National Science Foundation Grant ECCS 1711548. S. V. Dhople was supported in part by the National Science Foundation through grant CyberSEES 1442686.
© 2017 IEEE.