Nonlinear state and parameter estimation of spatially distributed systems
In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for id...
में बचाया:
| मुख्य लेखक: | |
|---|---|
| स्वरूप: | इलेक्ट्रोनिक पुस्तक अध्याय |
| भाषा: | अंग्रेज़ी |
| प्रकाशित: |
KIT Scientific Publishing
2009
|
| श्रृंखला: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory
|
| विषय: | |
| ऑनलाइन पहुंच: | DOAB: download the publication DOAB: description of the publication |
| टैग: |
टैग जोड़ें
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
|
| सारांश: | In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for identifying various model parameters. The Covariance Bounds Filter (CBF) allows the efficient estimation of widely distributed systems in a decentralized fashion. |
|---|---|
| भौतिक वर्णन: | 1 electronic resource (XI, 153 p. p.) |
| आईएसबीएन: | KSP/1000011485 9783866443709 |
| अभिगमन: | Open Access |