Multiple-Model Adaptive Estimation with A New Weighting Algorithm
The state estimation of a complex dynamic stochastic system is described by a discrete-time state-space model with large parameter (including the covariance matrices of system noises and measurement noises) uncertainties. A new scheme of weighted multiple-model adaptive estimation is presented, in w...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4789142 |
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| _version_ | 1832552483405168640 |
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| author | Weicun Zhang Sufang Wang Yuzhen Zhang |
| author_facet | Weicun Zhang Sufang Wang Yuzhen Zhang |
| author_sort | Weicun Zhang |
| collection | DOAJ |
| description | The state estimation of a complex dynamic stochastic system is described by a discrete-time state-space model with large parameter (including the covariance matrices of system noises and measurement noises) uncertainties. A new scheme of weighted multiple-model adaptive estimation is presented, in which the classical weighting algorithm is replaced by a new weighting algorithm to reduce the calculation burden and to relax the convergence conditions. Finally, simulation results verified the effectiveness of the proposed MMAE scheme for each possibility of parameter uncertainties. |
| format | Article |
| id | doaj-art-1fd66f3e7bea4f7fb228436852db8238 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1fd66f3e7bea4f7fb228436852db82382025-02-03T05:58:34ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/47891424789142Multiple-Model Adaptive Estimation with A New Weighting AlgorithmWeicun Zhang0Sufang Wang1Yuzhen Zhang2School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaThe state estimation of a complex dynamic stochastic system is described by a discrete-time state-space model with large parameter (including the covariance matrices of system noises and measurement noises) uncertainties. A new scheme of weighted multiple-model adaptive estimation is presented, in which the classical weighting algorithm is replaced by a new weighting algorithm to reduce the calculation burden and to relax the convergence conditions. Finally, simulation results verified the effectiveness of the proposed MMAE scheme for each possibility of parameter uncertainties.http://dx.doi.org/10.1155/2018/4789142 |
| spellingShingle | Weicun Zhang Sufang Wang Yuzhen Zhang Multiple-Model Adaptive Estimation with A New Weighting Algorithm Complexity |
| title | Multiple-Model Adaptive Estimation with A New Weighting Algorithm |
| title_full | Multiple-Model Adaptive Estimation with A New Weighting Algorithm |
| title_fullStr | Multiple-Model Adaptive Estimation with A New Weighting Algorithm |
| title_full_unstemmed | Multiple-Model Adaptive Estimation with A New Weighting Algorithm |
| title_short | Multiple-Model Adaptive Estimation with A New Weighting Algorithm |
| title_sort | multiple model adaptive estimation with a new weighting algorithm |
| url | http://dx.doi.org/10.1155/2018/4789142 |
| work_keys_str_mv | AT weicunzhang multiplemodeladaptiveestimationwithanewweightingalgorithm AT sufangwang multiplemodeladaptiveestimationwithanewweightingalgorithm AT yuzhenzhang multiplemodeladaptiveestimationwithanewweightingalgorithm |