两种新的有效的非线性系统最小二乘辨识算法
Two New Effective Bidiagonalization Least Squares Algorithms for Nonlinear System Identification
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摘要: 提出了两种新的有效的最小二乘算法--改进的双对角化最小二乘算法MBLS-Ⅰ 与MBLS-Ⅱ.在存在舍入误差的条件下,证明了算法的收敛性.该算法具有几乎不受舍入 误差影响的优点,优于一般常用的最小二乘算法.包括数值性态极佳的SVD算法.同时,基 于该算法及SVD算法,构造出了一种新的NARMAX模型结构与参数辨识的一体化算法. 仿真结果证明了此新算法的优越性.Abstract: Two new effective least squares algorithms--the modified bidiagonalization least squares algorithms(MBLS- I and MBLS-Ⅱ) are proposed in this paper. Under the condition that round-off errors exist, a convergence proof is given. They are superior to the common used least squares algorithms such as the SVD method for round-off errors have little influence to their convergence. Furthermore, based on the two algorithms and the SVD method, a new integrated algorithm for the NARMAX model's structure and parameters' identification is also proposed here. The simulation results indicate their superiority.
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Key words:
- Nonlinear system /
- system identification /
- bidiagonalization least squares
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