Analysising of the results predicted showed that the present method in convergence speed and precision are in line with requirements of on-line monitoring.
通过对预测结果的分析对比可知，该方法在收敛速度和精度方面均符合在线监测的要求。
The algorithm can remarkably improve the convergence speed and precision of the identification process compared with the partially decoupled RLS adaptive identification algorithm.
该算法与部分解耦的RLS自适应算法相比，显著提高了辨识过程的收敛速度和精度。
The advantage of the presented algorithm is that it possesses higher convergence speed and precision than the partly decoupled identification algorithm under a complete noise data environment.
相比于部分解耦辨识算法，该算法的优点在于它能够在全噪声数据环境下得到更高的收敛速度和精度。
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