基于最小二乘参数估计递推算法的线性系统模型参数估计 本科毕业论文.docVIP

CHANGZHOU INSTITUTE OF TECHNOLOGY 毕 业 设 计 说 明 书 题目:基于最小二乘参数估计递推算法的线性系统模型参数估计 二级学院（直属学部）： 延陵学院 专业： 自动化 班级： 07自Y 学生姓名： 张公宝 学号： 指导教师姓名： 马金祥 职称： 讲师 评阅教师姓名： 职称： 2011 年 6 月 摘 要 最小二乘参数估计的递推算法是系统参数辨识中最基本、最成熟的方法。对于给定的线性系统模型，设计一定幅值的M序列作为激励信号，设计服从正态分布的不相关随机噪声（噪信比满足一定要求）作为干扰信号，提取输出信号，并与输入信号进行分析处理，利用最小二乘参数估计递推算法对该模型进行参数辨识，然后针对论文的算法分别阐述了服从N（0,1）正态分布自相关随机噪声v（k）的产生方法。本文重点介绍了系统参数辨识中最小二乘法的基本原理，并通过设计一定幅值的M序列作为激励信号和服从正态分布的不相关随机噪声的线性模型作为辨识实例，具体说明了基于最小二乘法参数辨识递推算法在 Matlab中的实现方法。结果表明基于最小二乘法递推具有算法简单、精度较高等优点，并通过实验结果进行了详细的分析。从实验结果可以看出，实验数据符合实验要求，系统的参数辨识实现最小二乘参数估计的递推算法的效果令人满意。 关键词：最小二乘参数估计的递推算法；M序列；Matlab； Abstract Recursive least squares parameter estimation algorithm is the most basic identification of system parameters, the most sophisticated methods. For a given discrete model, the design of the M sequence as a certain amplitude excitation signal, the design is notrelated to follow a normal random noise (noise signal ratio to meet certain requirements) as the interference signal, extract the output signal, and analyzed with the input signal Processing, recursive least squares parameter estimation algorithm forparameter identification of the model, then the algorithm for the study were explained to obey N (0,1) normally distributed random noise autocorrelation v (k) of the method. This paper focuses on identification of system parameters the basic principle of least squares, and M through the design of a certain amplitude of the signal sequence as an incentive and follow a normal random noise is not related to identification of a linearmodel as examples, based on specified minimum Recursive least squares parameter identification algorithm in Matlabs implementation. The results show that the methodbased on recursive least squares algorithm is simple, the advantages of high precision, and through experimental results in detail. The results can be seen from the