Gradient optimization identification for state-space systems and convergence analysis
态空间系统的梯度优化辨识及收敛性分析*

In order to solve the problem which is caused by the nonlinearity and nonconvexity between the output error and the system parameters in state-space model, gradient optimization identification is proposed for parameter estimation of state-space systems. The principle of gradient identification based-on local linearization is analyzed. Moreover, the parameter search direction is determined based on the QR and SVD methods. And iterative identification algorithm is given for parameter estimation. Furthermore, the convergence of the identification algorithm is analyzed and the analytic expression of the convergence rate of the identification algorithm is also given. Finally, the effectiveness of the proposed method is illustrated by numerical simulation.