结构化动态系统的盲辨识:确定性方法及观点
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库存16件
广东广州
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作者俞成浦
出版社科学出版社
ISBN9787030781710
出版时间2024-04
四部分类子部>艺术>书画
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开本其他
定价168元
货号1203246887
上书时间2024-09-07
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目录
1 Introduction 1
1.1 Examples of the Blind System Identification 1
1.2 Optimization Based Blind System Identification 4
1.3 Blind Identification of Various System Models 5
1.4 Organization of This Book 6
References 8
Part I Preliminaries
2 Linear Algebra and Polynomial Matrices 11
2.1 Vector Space and Basis 11
2.2 Eigenvalue Decomposition 13
2.3 Singular Value Decomposition 15
2.4 Orthogonal Projection and Oblique Projection 16
2.5 Sum and Intersection of Subspaces 18
2.6 Angles Between Subspaces 19
2.7 Polynomial Matrices and Polynomial Bases 20
2.8 Summary 24
References 24
3 Representation of Linear System Models 25
3.1 Transfer Functions 25
3.1.1 Properties of Coprime Matrix Fraction 26
3.1.2 Verification and Computation of Coprime Matrix Fraction 28
3.2 State Space Models 31
3.3 State Space Realization 38
3.4 HankelMatrix Interpretation 40
3.5 Structured State-Space Models 41
3.5.1 Graph Theory 42
3.5.2 Structured Algebraic System Theory 44
3.6 Summary 47
Reference 48
4 Identification of LTI Systems 49
4.1 Least-Squares Identification 50
4.1.1 Identifiability of a Rational Transfer Function Matrix 50
4.1.2 Least-Squares Identification Method 51
4.2 Subspace Identification 53
4.2.1 Subspace Identification via Orthogonal Projection 55
4.2.2 Subspace Identification via State Estimation 56
4.2.3 Subspace Identification via State Compensation 59
4.2.4 Subspace Identification via Markov Parameter Estimation 61
4.3 Parameterized State-Space Identification 62
4.3.1 Gradient-BasedMethod 63
4.3.2 Difference-of-Convex Programming Method 64
4.4 Summary 69
References 70
Part II Blind System Identification with a Single Unknown Input
5 Blind Identification of SIMO FIR Systems 73
5.1 Structured Subspace Factorization 74
5.1.1 Blind Identification of FIR Filters 75
5.1.2 Blind Identification of a Source Signal 78
5.2 Cross RelationMethod 80
5.3 Least-Squares Smoothing Method 83
5.3.1 Blind FIR Filter Identification 84
5.3.2 Blind Source Signal Estimation 85
5.4 Blind Identification of Time-Varying FIR Systems 86
5.4.1 Input Signal Estimation 87
5.4.2 Time-Varying Filter Identification 88
5.5 Blind Identification of Nonlinear SIMO Systems 90
5.5.1 SIMO-Wiener System Identification 91
5.5.2 Hammerstein-Wiener System Identification 93
5.6 Summary 94
References 95
6 Blind Identification of SISO IIR Systems via Oversampling 97
6.1 Oversampling of FIR and IIR Systems 98
6.1.1 Multirate Identities 98
6.1.2 Multirate Transfer Functions 99
6.1.3 Multirate State-Space Models 103
6.2 Coprime Conditions for Lifted SIMO Systems 104
6.3 Blind Identification of Non-minimum Phase Systems 108
6.4 Blind Identification of Hammerstein Systems 110
6.4.1 Blind Identifiability 111
6.4.2 Blind Identification Approach 112
6.5 Blind Identification of Output Switching Systems 114
6.6 Summary 125
References 126
7 Distributed Blind Identification of Networked FIR Systems 127
7.1 Motivation for the Distributed Blind Identification 127
7.2 Distributed Blind System Identification Using Noise-Free Data 128
7.2.1 Distributed Blind Identification Algorithm 129
7.2.2 Convergence Analysis 131
7.2.3 Numerical Simulation 136
7.3 Distributed Blind System Identification Using Noisy Data 138
7.3.1 Distributed Blind Identification Algorithm 139
7.3.2 Convergence Analysis 140
7.3.3 Numerical Simulation 147
7.4 Recursive Blind Source Equalization Using Noisy Data 148
7.4.1 Direct Distributed Equalization 149
7.4.2 Indirect Distributed Equalization 151
7.4.3 Distributed Blind Equalization with Noise-Free Measurements 152
7.4.4 Distributed Blind Equalization with Noisy Measurements 156
7.4.5 Blind Equalization with a Time-Varying Topology 157
7.4.6 Numerical Simulation 159
7.5 Summary 162
References 163
Part III Blind System Identification with Multiple Unknown Inputs
8 Blind Identification of MIMO Systems 167
8.1 Blind Identification ofMIMO FIR Systems 167
8.1.1 Identifiability Analysis 169
8.1.2 Subspace Blind Identification Method 171
8.2 Blind Identification of Multivariable State-Space Models 173
8.2.1 Identifiability of Two Channel Systems 174
8.2.2 Blind Identification of Characteristic Polynomials 179
8.2.3 Blind Identification of Numerator Polynomial Matrices 183
8.2.4 Numerical Simulation 192
8.3 Summary 197
References 198
9 Blind Identification of Structured State-Space Models 199
9.1 Strong Observability of Structured State-Space Models 199
9.1.1 Maximum Unobservable Subspace 200
9.1.2 State Estimation with Unknown Inputs 202
9.2 Blind Identification of Multivariable State-Space Models 204
9.2.1 Identifiability Analysis 206
9.2.2 Subspace-Based Blind Identification Method 215
9.2.3 Numerical Simulations 220
9.3 Blind System Identification Excited by Different Unknown Inputs 225
9.3.1 Identifiability Analysis 226
9.3.2 Subspace Identification Method 230
9.4 Summary 231
References 231
10 Blind Local Identification of Large-Scale Networked Systems 233
10.1 Local Network Identification 233
10.2 Subspace Identification Approach 235
10.3 Subspace Identification of Unknown Inputs 240
10.3.1 Estimation of Completely Unmeasurable Inputs 240
10.4 Numerical Simulations 254
10.5 Summary 256
References 257
11 Conclusions 259
11.1 About the Identification Object 259
11.2 About the Identifiability Analysis 261
11.3 About the Identification Method 261
11.4 Artificial Intelligence Driven Blind System Identification 262
References 264
Index 265
内容摘要
本书全面且深入地研究盲系统辨识问题，通过利用系统模型的结构特性，提供确定性辨识解决方案，以揭示相关数值计算的基本代数性质。基于子空间的辨识方法是处理传统盲辨识问题和经典状态空间辨识问题的一种常用方法，它将被广泛应用和推广，以解决若干具有挑战性的结构化系统盲辨识问题。从很优化的角度看，子空间辨识技术可以看做是求解低秩矩阵分解或低秩极小化问题的方法，但它不能处理具有结构约束的动态系统的盲辨识问题。针对这一问题，提出了一种差分凸规划方法，该方法比传统的基于梯度的优化方法能得到更可靠的辨识结果。总之，本书旨在为处理具有挑战性的系统事变问题提供独到深刻的求解思路/见解。
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