In this dissertation , some problems of system identification have been studied . the researches are focused on tracking problems of time - varying systems 本文对系统辨识的若干问题进行了深入的研究，重点研究了时变系统的跟踪问题。
Compared with second - order statistics , higher - order statistics not only reveal information about a process , but also are blind to any kind of a gaussian process 其中，非高斯、非线性、非平稳和时变系统等复杂的系统辨识问题正在成为人们研究的热门课题。
For a large scale of linear time - invariant systems , linear time - varying systems and nonlinear systems , many scholars proposed a series of new schemes 针对一大类线性时不变系统，线性时变系统及非线性系统，许多学者利用backstpping方法提出了一系列新的非线性控制器设计方案
There are many nonlinear systems with their characters slow time varying in control domain . those with intrinsical nonlinear portions are even much more hard to control successfully 在控制领域中，存在着大量的非线性慢时变系统，其中的带有本质非线性环节的系统尤其难以得到好的控制效果。
It ' s still a difficult problem to track the time - varying system because of the complexity of time - varying system and the limitation of people accessing infonnation from it 由于时变系统的广泛性和复杂性，以及人们从时变系统获得信息的局限性，时变系统的跟踪仍是一个有许多问题要解决的难题。
Numerical resultsdemonstrate the accuracy and the validity of the proposed method for identificationthe parameters of lti system and smoothly , periodically and abruptly time - varyingparameters of ltv system 对线性时不变系统和线性时变系统在参数连续平滑变化、周期变化、突变的情形下进行仿真验证。
In addition , morse proved that tenable system must be a minimum phase system . for linear time varying systems , tskalis and loannou ( 1992 , 1989 ) proposed mrac design method 为了保证可调性，被控对象必须是最小相位的，对于线性时变系统， tskalis与ioannou 992 ， 1989 ）给出了模型参考自适应控制器的设计方法
Based on the statere configuration system , an unknown input observer is presented for the prediction of time series . the approximate error of the ar model is regarded as the unknown - input of system 以实时拟合时间序列的线性ar模型作为时变系统的已知线性部分,将拟合误差作为时变系统的未知输入,实现了系统状态的多步预测。
As a case study , this method has the merits of both multi - degree and regression analysis , reflecting the important function of the related factor in the prediction mode as well as the strong adaptability to the system of sequential valance 实际应用中，该方法集多层递阶和回归分析两者的优点，既能体现高相关因子在预测模型中的重要作用，同时对时变系统又具有较强的适应性。
Aiming at linear time - varying , systems of which the static state characteristic is known , and through the analysis of the dynamic response data , a new linear model jumped by time is set up and an automatic optimization identification algorithm is programmed 摘要基于静态特性已知的线性时变系统，通过对其动态响应数据的分析，建立了随时间跳变的动态参数模型，提出了一种新的线性自寻优辨识算法。
