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The filtering based parameter identification for bilinear-in-parameter systems
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DOI：10.1016/j.jfranklin.2018.11.005.png)
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摘要
En 中文
This paper discusses the parameter estimation for a class of bilinear-in-parameter systems with colored noise. By utilizing the filtering technique, we derive the relationship between the filtered output and the measurement output and obtain two linear regressive sub-models. A filtering based multi-innovation stochastic gradient algorithm is derived for interactively identifying each sub-model. The proposed algorithm avoids the estimation of correlated noise and improves the parameter estimation accuracy by making full use of the measurement data. The numerical simulation results indicate that the proposed algorithm has higher estimation accuracy than the hierarchical multi-innovation stochastic gradient algorithm. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keyword:
LEAST-SQUARES ALGORITHM
OPTIMIZATION
CONVERGENCE
DESIGN
MODEL
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IF:
3.7
论文数:
6.5K
被引数:
1.5W
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