Young, Peter C. and McKenna, Paul and Bruun, John (2001) Identification of non-linear stochastic systems by state dependent parameter estimation. International Journal of Control, 74 (18). pp. 1837-1857.Full text not available from this repository.
The paper outlines how improved estimates of time variable parameters in models of stochastic dynamic systems can be obtained using recursive filtering and fixed interval smoothing techniques, with the associated hyper-parameters optimized by maximum likelihood based on prediction error decomposition. It then shows how, by exploiting special data re-ordering and back-fitting procedures, similar recursive parameter estimation techniques can be utilized to estimate much more rapid State Dependent Parameter (SDP) variations. In this manner, it is possible to identify and estimate a widely applicable class of nonlinear stochastic systems, as illustrated by several examples that include simulated and real data from chaotic processes. Finally, the paper points out that such SDP models can form the basis for new methods of signal processing, automatic control and state estimation for nonlinear stochastic systems.
|Journal or Publication Title:||International Journal of Control|
|Subjects:||G Geography. Anthropology. Recreation > GE Environmental Sciences|
|Departments:||Faculty of Science and Technology > Lancaster Environment Centre|
Faculty of Science and Technology
|Deposited On:||22 Jan 2009 11:38|
|Last Modified:||07 Jan 2015 13:28|
Actions (login required)