论文部分内容阅读
考虑在输入-输出数据都带有噪声的前提下,将偏差补偿最小二乘算法(CLS)进行推广得到非线性可分离的最小二乘算法(NSLS)。采用适用于噪声环境的非线性可分离的最小二乘算法可准确地辨识飞机的颤振模态参数,该算法结合传递函数模型,将带噪声系统的辨识问题转化为非线性可分离的最小二乘问题。利用该算法,两噪声的方差值和传递函数中的模型参数可分离地估计出来。最后利用试飞试验数据辨识飞机的系统参数,验证了该方法的有效性。
The nonlinear separable least squares (NSLS) algorithm is derived by generalizing the bias compensation least squares algorithm (CLS) on the assumption that the input and output data are all noise. The nonlinear separable least-squares algorithm for noise environment can be used to accurately identify the flutter modal parameters of the aircraft. The algorithm, combined with the transfer function model, transforms the identification of noisy systems into the nonlinear separable minimum two Take the question. Using this algorithm, the variance of the two noises and the model parameters in the transfer function can be estimated separately. Finally, the flight test data is used to identify the system parameters of the airplane, which verifies the effectiveness of the method.