中文摘要：

针对传统最小二乘算法计算量大、在有色噪声干扰下估计有误差的问题,提出了一种基于滤波技术的带协方差重置的递推贝叶斯算法。该算法使用一个动态非线性滤波器对输入输出数据进行滤波,然后使用贝叶斯方法进行参数估计。为了加快参数的收敛速度,在算法中加入了一种新型的协方差重置策略。计算量分析表明,当过程模型和噪声模型的阶数分别为6和4的时候,所提算法可以减少约62.35%的计算量。仿真结果显示,所提算法与传统最小二乘算法在采样数据长度为3 000时的估计误差分别为0.771%和1.118%。因此,所提算法具有较高的计算效率,并且可以给出精度较高的参数估计值。

英文摘要：

Traditional least squares identification algorithm required much computational cost and its estimates were biased when the noise was colored. To overcome these shortcomings,this paper proposed a filter based recursive Bayesian identification algorithm with covariance resetting. In this algorithm,it firstly filtered the input and output data by a dynamics nonlinear filter and then used recursive Bayesian algorithm to estimate parameters. It also integrated a modified covariance resetting method to the algorithm. Analysis revealed that the proposed algorithm could reduce the computational burden by 62. 35% compared with recursive Bayesian algorithm. Simulations indicate that the estimation errors of the two algorithms are 0. 771% and 1. 118% respectively. So the proposed algorithm has higher efficiency and can generate estimates with higher accuracy.