有色噪声条件下的子空间辨识改进方法及应用

针对工业生产过程中噪声往往为有色噪声的情况，提出一种改进的子空间辨识方法。传统的子空间辨识方法在系统存在有色噪声时辨识效果不佳，改进方法则采用变换系统模型形式来克服有色噪声对系统的影响，在辨识时直接利用变换系统模型后的数据得到系统较为准确的状态空间模型，实践证明，状态空间模型更适用于工业过程。连续搅拌反应釜（CSTR）系统是一类典型的工业生产系统，将子空间辨识方法应用于CSTR过程的仿真实验，通过比较改进前和改进后的系统预测误差，验证了所提方法的有效性。

Improved subspace identification method in presence of non-stationary noise and its application

In order to deal with non-stationary noise in the industrial production processes, an improved subspace identification method is proposed. The identification result is not efficient in presence of non-stationary noise through traditional subspace identification method. So the transformational system model form is used to eliminate the effect of non-stationary noise on system through improved subspace identification method. The accurate state-space model is obtained through identification using the transformational system model data. Practice has proved, the state-space model is more suitable for industrial processes. CSTR is a typical industrial production system and the subspace identification method is applied to the process simulation on a CSTR. Through comparisons of the predication error before and after improved, the effectiveness of the proposed method is showed.