受控自回归系统的带惩罚项梯度辨识算法

Gradient identification algorithms based on the penalty criterion function for controlled autoregressive systems

基于带惩罚项准则函数,研究受控自回归系统的辨识问题.首先,通过负梯度搜索,极小化带惩罚项的准则函数,得到计算参数估计的递推关系,并利用一维线搜索确定最佳步长,推导带惩罚项投影梯度辨识算法和带惩罚项随机梯度辨识算法;然后,为了提高带惩罚项随机梯度算法的收敛速度,使用多新息辨识理论,推导带惩罚项多新息随机梯度辨识算法;最后,通过仿真实例验证所提出算法的有效性.

Based on the criterion function with penalty,the identification problem of controlled autoregressive systems is studied.Through the gradient search,the recursive relationship of parameter estimation is obtained by minimizing the criterion function with penalty,and the optimal step-size is deducted to obtain the parameter estimation through the one-dimensional line search.The projection identification algorithm with penalty and the stochastic gradient identification algorithm with penalty are proposed.By using the multi-innovation theory,a multi-innovation stochastic gradient identification algorithm with penalty is proposed.The effectiveness of the proposed algorithm is verified by the simulation.