系统辨识是研究用实验数据建立动态系统数学模型的学科，在多个领域特别是自动控制领域有广泛的应用。系统辨识的渐进法以L.Ljung的渐进理论为基础，能够全面解决系统辨识的四个基本问题：实验设计、模型阶的确定、参数估计和模型校验。渐进法已在国外石油化工工业及其他流程工业有广泛应用。本项目拟将渐进法向两个方向扩展：. 1. Errors-in-variables （EIV）系统辨识。该模型的输入信号含有测量噪声,现有的辨识方法还不成熟。本项目首先将渐进理论推广到EIV模型，然后应用该理论导出渐进法。新方法将突破现有方法局限，更准确，可进行闭环辨识。. 2. Wiener-Hammerstein模型的辨识。该模型是一种块结构的非线性模型，参数估计中数值计算很困难，没有成熟的方法。本工作将研究使用渐进法的数值计算技巧解决该模型的参数估问题

System identification is the field of building mathematical models using test data which has wide application in many diciplines especially in automatic control. The so-called asymptotic method (ASYM) was developed based on the asymptotic theory of L. Ljung. The ASYM can systematically solve the four problems of identification and has been applied in the petrochecmical industry and other process industries. In this project, we will extend the ASYM for two model types.. 1. Errors-invariable model identification. In this model, the input signals contain measurement noises and existing methods are not yet mature. This work will first extend the asymptotic theory to the errors-in-variables models and then develop the ASYM method for the model class. The new method will be more accurate than existing methods and can be used in closed-loop identificatio.. 2. Wiener-Hammestein model identification. This is a block roiented nonlinear model and the parameter estimation is very difficult due to its compex structure. In this work, the numeric techniques of the ASYM method will be applied to solve the parameter estimation problem for the Wiener-hammerstein model.