系統(tǒng)辨識(shí)理論及應(yīng)用

內(nèi)容概要

　　本書主要闡述系統(tǒng)辨識(shí)的基本原理以及應(yīng)用。本書共分14章。第1章至第4章為緒論、系統(tǒng)辨識(shí)常用輸入信號(hào)、線性系統(tǒng)的經(jīng)典辨識(shí)方法和動(dòng)態(tài)系統(tǒng)的典范表達(dá)式，主要回顧和介紹了與系統(tǒng)的辨識(shí)有關(guān)的一些基礎(chǔ)知識(shí)。第5章至第12章為最小二乘法辨識(shí)、極大似然法辨識(shí)、時(shí)變參數(shù)辨識(shí)方法、多輸入—多輸出系統(tǒng)的辨識(shí)、其他一些辨識(shí)方法、隨機(jī)時(shí)序列模型的建立、系統(tǒng)結(jié)構(gòu)辨識(shí)和閉環(huán)系統(tǒng)辨識(shí)等，介紹了系統(tǒng)辨識(shí)常用基本方法，是系統(tǒng)辨識(shí)的主要內(nèi)容。第13章和第14章分別介紹了系統(tǒng)辨識(shí)在飛行器參數(shù)辨識(shí)中的應(yīng)用和神?網(wǎng)絡(luò)在系統(tǒng)辨識(shí)中的應(yīng)用。

書籍目錄

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

章節(jié)摘錄

版權(quán)頁(yè)：插圖：The aerodynamic forces on the aircraft decide the motion states of the aircraft, and the flight states satisfy a six freedom motion equation set derived according to Newton's second law, and the equation set is an ordinary differential equation set in which time t is an argument, so the aerodynamic parameter identification belongs to the parameter identification of the centralized-parameter system. The aerodynamic heat on the aircraft decides the temperature distribution course on the aircraft, and it is a function of not only time but also space position. The state equation set of the system is a partial differential equation set derived according to the thermal conductance law and the energy conservation law, so the aero thermodynamic parameter identification belongs to the distributed parameter identification and to the function identification.Objective of the aerodynamic parameter identification is to establish mathematical models of aerodynamic coefficients, amely to establish relation between the aerodynamic coefficients and the parameters of aircraft. The relation may be algebraic equation, differential equation or integral equation. The first established aerodynamic mathematic model is a linear algebraic equation, and it only is applicable to small attack angle state of aircraft. The linear models have got extensive applications in development of aircraft, and up to now they still are the foundations for analyses of aircraft stability, flight quality and flight performance. Development of the linear aerodynamic parameter identification has been very mature, and main development branches of aircrafts in all the country have stock themselves software packages for linear aerodynamic parameter identification, among them the most practical and most effective one is the software package for maximumikelihood identification. When the aircraft is located in large attack angle flight phase, for example, in stalling or tail spinning phase of an airplane and in large maneuvering phase of a tactical missile, the linear aerodynamic models are inapplicable. Up to now, various forms of nonlinear aerodynamic mathematical models, such as polynomial, spline function, step response function, differential equation and so on, have been investigated. At present the focal point of the research work for the aerodynamic parameter identification is to investigate algorithms and applications of parameter identification for non-steady aerodynamic delayed effect, nonlinear aerodynamic parameters and nonlinear closed-loop systems.

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