《系统辨识理论及应用》是国防工业出版社2011年2月1日出版来自的图书，作者是李言俊 、张360百科科 、 余瑞星 。
来自《系统辨识理论及应用》主要阐述系统辨识的基本原理以及应用。《系统辨识理论及应用》共分14章。第1章至第4章为绪论、系统辨识常用输入信号、线性系统的经典辨识方法和动态系统的典范表达式，主要回顾和介绍了与系统的辨识有关的一些基础知识。第5章至第12章为最小二乘法辨识、极大似然法辨识、时变参数辨识方法、多输入一多输出系统的辨识、其他一360百科些辨识方法、随机时序列模型的建立、系统结构辨识和闭环系统辨识等，介绍了系统辨识常用基本方法，是系统辨识的主要内容。第13章和第14章分别介绍买了系统辨识在飞行器参数辨识中的应用和神经网络在系统辨识中的应用。
于策跟银请云早女激什胜 Chapter 1 Introduction
1.1 Classification of Mathematic Models of System and Modelling Methods
1.1.1 Signification 宣紧项总香胶真行美of Model
1.1.2 Representation For成由附世里候河黄越ms of Models
1样讲思调.1.3 Classification of Mathematic Model家汽想苦跟易七s
1.1.4 Basic Methods to Establish Mathematic Model
1.1.5 盟线备季亚真血Basic Principles Followed for Modeling
1.2 Definition, Content and Procedure of Identific可聚施师向钱酒ation
1.2.1 Definition of Identificauon
1.2.2 Content and Procedure of Identification
1.3 Error Criteria Usually Used in Identification
1.3.1 O神轮笑基刻utput Error Criterion
1.3.2 府限父也情迫及候花司切Input Error Crit却声言分处技成erion
1.3.3 Generalized Er排ror Criteri轴升on
1.4 C介视lassification of System Identification:
1.4.1 Off-line Identification
1.4.剧露春景2 On-line Identificatio众球种容纸父n
Problems
Chapter 2 Commonly Used Inp护师室粮皇松记着结胶黑ut Signals for Sys背联黄tem Identification
2.1 Selective Criteria of Input Signal for System Identification
2.2 White Noises and Its Generating Methods
2.2.1 White Noise Process
2.2.2 White Noise Sequence
2.2.3 Generating Methods of White Noise Sequence
2.3 Generation of Pseudorandom Binary Sequence--M-Sequence and Its Properties
2.3.1 Pseudorandom Noise
2.3.2 Generating Method of M-Sequence
2.3.3 Properties of M-Sequence;
2.3.4 Autocorrelation Function of Two-Level M-sequence
2.3.5 Power Spectral Density of Two-Level M-sequence
Problems
Chapter 3Classical Identification Methods of Linear System
3.1 Identify Impulse Response of Linear System by Use of M-Sequence
3.2 Obtain Transfer Function from Impulse Function,
3.2.1 Transfer Function G(s) of Continuous System
3.2.2 Transfer Function of Discrete System--Impulse Transfer Function G(z-1 )
Problems
Chapter 4 Canonical Expression of Dynamic Systems
4.1 Parsimony Principle~
4.2 Representations of Difference Equation and State Equation of Linear System
4.2.1 Representation of Difference Equation of Linear ,Time-Invariant System
4.2. 2Representation of State Equation of Linear System
4.3Deterministic Canonical State Equations
4.3.1 Controllable Form of Canonical State Equation
4.3.2 Controllable Form of Canonical State Equation
4.3.3 Observable Form of Canonical State Equation I
4.3.4 Observable Form of Canonical State Equation II
4.3.5 Observable Form of Canonical State Equation I of Mimo System
4.3.6 Observable Form of Canonical State Equation II of Mimo System
4.4 Deterministic Canonical Difference Equationsr
4.5 Stochastic Canonical State Equations
4.6 Stochastic Canonical Difference Equations
4.7 Prediction Error Equation
Problems
Chapter 5 Least-Squares Identification
5.1 Least Square Method
5.1.1 Algorithns of Least2Square Estimation
5.1.2 Input Signals for Least-Squares Estimation
5.1.3 Probability Properties of Least-Squares Estimation
5.2 A Kind of Least Squres Which Need Not Invert Matrix
5.3 Recursive Least Squares
5.4 Auxiliary Variable Method
5.5 Recursive Auxiliary Variable Method
5.6 Generalized Least Squares
5.7 An Alternative Generalized Least Squares Technique (Hsia Method)
5.8 Extended Matrix Method
5.9 Multistage Least Squares
5.9.1 The First Algorithm
5.9.2 The Second Algorithm
5.9.3 The Third Algorithm
5.10 Fast Multistage Least Squares Problems
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