| 李言俊编著的《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。本书内容简明扼要，理论紧密结合实际，较系统地阐述了系统辨识的主要步骤和内容，具有很好的实用性。本书可供从事系统建模的研究人员，工程师，研究生和相关专业的技术人员阅读参考。 |
| Chapter 1 introduction 1.1 Classification of Mathematic Models of System and Modelling Methods 1.1.1 Signification of Model 1.1.2 Representation Forms of Models 1.1.3 Classification of Mathematic Models 1.1.4 Basic Methods to Establish Mathematic Model 1.1.5 Basic Principles Followed for Modeling 1.2 Definition, Content and Procedure of Identification 1.2.1 Definition of Identification 1.2.2 Content and Procedure of Identification 1.3 Error Criteria Usually Used in Identification 1.3.1 Output Error Criterion 1.3.2 Input Error Criterion 1.3.3 Generalized Error Criterion 1.4 Classification of System Identification 1.4.1 Off-line Identification 1.4.2 On-line Identification Problems Chapter 2 Commonly Used Input Signals for System 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 3 Classical 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 4.2.2 Representation of State Equation of Linear System 4.3 Deterministic Canonical State Equations 4.3.1 Controllable Form of Canonical State Equation I 4.3.2 Controllable Form of Canonical State Equation II 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 Equations 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 Least-Square 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 …… Chapter 6 Maximum-Likelihood Identification Chapter 8 Identification of Multi-Input Multi-Output Systems Chapter 9 Some Other Kinds of Identification Methods Chapter 10 Establishment of Random Time Series Models Chapter 11 Structure Identification of System Chapter 12 Identification of Closed-Loop System Chapter 13 Application of System Identification to Parameter Identification of Aircraft Chapter 14 Applicatiom of Neural Network to System Identification References |
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