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A fast nonlinear model identification method
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DOI：10.1109/TAC.2005.852557.png)
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摘要
En 中文
The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.
Keyword:
computational complexity
fast recursive algorithm
nonlinear system identification
numerical stability
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IF:
7
论文数:
1.3W
被引数:
6.7W
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