一种改进的递推极大似然法

A modified Recursive Maximum Likelihood Method

在传统CARMA模型的基础上,用RLS法拟合高阶CAR模型单独得到白噪声估值,并将之应用于RML算法中。仿真实验结果表明,改进后的RML算法极大提高了算法的精度,具有较强的适应性、稳定性和灵活性,收敛速度快,有效解决了滑动平均模型参数估值收敛较慢的问题,在预报和控制领域中具有一定的实用价值。

In this paper,modified recursive maximum Iikehood(RML)parameter estimation algorithm is based on the traditional CARMA model,and the RLS method is used to fit the high-order CAR model separately to get the white noise valuation,which is then applied to the RML algorithm.Simulation results show that the improved RML algorithm greatly improves the accuracy of the algorithm,strong adaptability,stability and flexibility,converges quickly,and the problem of slow convergence of sliding average model parameter estimation is solved effectively.It has certain practical value in the field of forecasting and control.