Identification of nonlinear physiological systems:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Piscataway, N.J.
IEEE Press
2003
Hoboken, NJ Wiley-Interscience |
| Schriftenreihe: | IEEE Press series on biomedical engineering
|
| Schlagworte: | |
| Online-Zugang: | Contributor biographical information Publisher description Table of contents Inhaltsverzeichnis |
| Beschreibung: | "IEEE Engineering in Medicine and Biology Society, Sponsor." Includes bibliographical references (p. 251-257) and index |
| Beschreibung: | XII, 261 S. Ill. 25 cm |
| ISBN: | 9780471274568 0471274569 |
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| 245 | 1 | 0 | |a Identification of nonlinear physiological systems |c David T. Westwick, Robert E. Kearney |
| 264 | 1 | |a Piscataway, N.J. |b IEEE Press |c 2003 | |
| 264 | 1 | |a Hoboken, NJ |b Wiley-Interscience | |
| 300 | |a XII, 261 S. |b Ill. |c 25 cm | ||
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| 500 | |a "IEEE Engineering in Medicine and Biology Society, Sponsor." | ||
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Datensatz im Suchindex
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| adam_text | CONTENTS
Preface xi
1 Introduction 1
1.1 Signals / 1
1.1.1 Domain and Range / 2
1.1.2 Deterministic and Stochastic Signals / 2
1.1.3 Stationary and Ergodic Signals / 3
1.2 Systems and Models / 3
1.2.1 Model Structure and Parameters / 4
1.2.2 Static and Dynamic Systems / 5
1.2.3 Linear and Nonlinear Systems / 6
1.2.4 Time Invariant and Time Varying Systems / 7
1.2.5 Deterministic and Stochastic Systems / 7
1.3 System Modeling / 8
1.4 System Identification / 8
1.4.1 Types of System Identification Problems / 9
1.4.2 Applications of System Identification / 11
1.5 How Common are Nonlinear Systems? / 11
2 Background 13
2.1 Vectors and Matrices / 13
2.2 Gaussian Random Variables / 14
2.2.1 Products of Gaussian Variables / 15
2.3 Correlation Functions / 16
v
Vi CONTENTS
2.3.1 Autocorrelation Functions / 16
2.3.2 Cross Correlation Functions / 18
2.3.3 Effects of Noise / 20
2.3.4 Estimates of Correlation Functions / 21
2.3.5 Frequency Domain Expressions / 22
2.3.6 Applications / 23
2.3.7 Higher Order Correlation Functions / 25
2.4 Mean Square Parameter Estimation / 25
2.4.1 Linear Least Squares Regression / 26
2.4.2 Properties of Estimates / 27
2.5 Polynomials / 29
2.5.1 Power Series / 29
2.5.2 Orthogonal Polynomials / 30
2.5.3 Hermite Polynomials / 31
2.5.4 Tchebyshev Polynomials / 32
2.5.5 Multiple Variable Polynomials / 33
2.6 Notes and References / 35
2.7 Problems / 36
2.8 Computer Exercises / 36
3 Models of Linear Systems 39
3.1 Linear Systems / 39
3.2 Nonparametric Models / 40
3.2.1 Time Domain Models / 41
3.2.2 Frequency Domain Models / 43
3.3 Parametric Models / 46
3.3.1 Parametric Frequency Domain Models / 46
3.3.2 Discrete Time Parametric Models / 48
3.4 State Space Models / 52
3.4.1 Example: Human Ankle Compliance—Discrete Time,
State Space Model / 54
3.5 Notes and References / 54
3.6 Theoretical Problems / 55
3.7 Computer Exercises / 56
4 Models of Nonlinear Systems 57
4.1 The Volterra Series / 57
4.1.1 The Finite Volterra Series / 59
4.1.2 Multiple Input Systems / 62
4.1.3 Polynomial Representation / 64
4.1.4 Convergence Issues(^) / 65
CONTENTS Vii
4.2 The Wiener Series / 67
4.2.1 Orthogonal Expansion of the Volterra Series / 68
4.2.2 Relation Between the Volterra and Wiener Series / 70
4.2.3 Example: Peripheral Auditory Model—Wiener Kernels / 71
4.2.4 Nonwhite Inputs / 73
4.3 Simple Block Structures / 73
4.3.1 The Wiener Model / 73
4.3.2 The Hammerstein Model / 77
4.3.3 Sandwich or Wiener Hammerstein Models / 79
4.3.4 NLN Cascades / 83
4.3.5 Multiple Input Multiple Output Block Structured Models / 87
4.4 Parallel Cascades / 87
4.4.1 Approximation Issues(^) / 89
4.5 The Wiener Bose Model / 91
4.5.1 Similarity Transformations and Uniqueness / 92
4.5.2 Approximation Issues(f) / 94
4.5.3 Volterra Kernels of the Wiener Bose Model / 94
4.5.4 Wiener Kernels of the Wiener Bose Model / 95
4.5.5 Relationship to the Parallel Cascade Model / 97
4.6 Notes and References / 100
4.7 Theoretical Problems / 100
4.8 Computer Exercises / 101
5 Identification of Linear Systems 103
5.1 Introduction / 103
5.1.1 Example: Identification of Human Joint Compliance / 103
5.1.2 Model Evaluation / 105
5.2 Nonparametric Time Domain Models / 107
5.2.1 Direct Estimation / 107
5.2.2 Least Squares Regression / 108
5.2.3 Correlation Based Methods / 109
5.3 Frequency Response Estimation / 115
5.3.1 Sinusoidal Frequency Response Testing / 115
5.3.2 Stochastic Frequency Response Testing / 116
5.3.3 Coherence Functions / 117
5.4 Parametric Methods / 119
5.4.1 Regression / 119
5.4.2 Instrumental Variables / 120
5.4.3 Nonlinear Optimization / 121
5.5 Notes and References / 122
5.6 Computer Exercises / 122
Viii CONTENTS
6 Correlation Based Methods 125
6.1 Methods for Functional Expansions / 125
6.1.1 Lee Schetzen Cross Correlation / 125
6.1.2 Colored Inputs / 140
6.1.3 Frequency Domain Approaches / 144
6.2 Block Structured Models / 149
6.2.1 Wiener Systems / 150
6.2.2 Hammerstein Models / 155
6.2.3 LNL Systems / 162
6.3 Problems / 167
6.4 Computer Exercises / 167
7 Explicit Least Squares Methods 169
7.1 Introduction / 169
7.2 The Orthogonal Algorithms / 169
7.2.1 The Orthogonal Algorithm / 171
7.2.2 The Fast Orthogonal Algorithm / 173
7.2.3 Variance of Kernel Estimates / 180
7.2.4 Example: Fast Orthogonal Algorithm Applied to Simulated Fly
Retina Data / 182
7.2.5 Application: Dynamics of the Cockroach Tactile Spine / 186
7.3 Expansion Bases / 187
7.3.1 The Basis Expansion Algorithm / 190
7.3.2 The Laguerre Expansion / 191
7.3.3 Limits on a I 192
7.3.4 Choice of a and P I 194
7.3.5 The Laguerre Expansion Technique / 195
7.3.6 Computational Requirements / 195
7.3.7 Variance of Laguerre Kernel Estimates / 195
7.3.8 Example: Laguerre Expansion Kernels of the Fly
Retina Model / 196
7.4 Principal Dynamic Modes / 198
7.4.1 Example: Principal Dynamic Modes of the Fly
Retina Model / 200
7.4.2 Application: Cockroach Tactile Spine / 201
7.5 Problems / 205
7.6 Computer Exercises / 205
8 Iterative Least Squares Methods 207
8.1 Optimization Methods / 207
8.1.1 Gradient Descent Methods / 208
CONTENTS ix
8.1.2 Identification of Block Structured Models / 209
8.1.3 Second Order Optimization Methods / 212
8.1.4 Jacobians for Other Block Structures / 216
8.1.5 Optimization Methods for Parallel Cascade Models / 219
8.1.6 Example: Using a Separable Volterra Network / 220
8.2 Parallel Cascade Methods / 223
8.2.1 Parameterization Issues / 226
8.2.2 Testing Paths for Significance / 228
8.2.3 Choosing the Linear Elements / 230
8.2.4 Parallel Wiener Cascade Algorithm / 242
8.2.5 Longer Cascades / 242
8.2.6 Example: Parallel Cascade Identification / 243
8.3 Application: Visual Processing in the Light Adapted Fly Retina / 246
8.4 Problems / 249
8.5 Computer Exercises / 250
References 251
Index 259
IEEE Press Series in Biomedical Engineering 262
|
| adam_txt |
CONTENTS
Preface xi
1 Introduction 1
1.1 Signals / 1
1.1.1 Domain and Range / 2
1.1.2 Deterministic and Stochastic Signals / 2
1.1.3 Stationary and Ergodic Signals / 3
1.2 Systems and Models / 3
1.2.1 Model Structure and Parameters / 4
1.2.2 Static and Dynamic Systems / 5
1.2.3 Linear and Nonlinear Systems / 6
1.2.4 Time Invariant and Time Varying Systems / 7
1.2.5 Deterministic and Stochastic Systems / 7
1.3 System Modeling / 8
1.4 System Identification / 8
1.4.1 Types of System Identification Problems / 9
1.4.2 Applications of System Identification / 11
1.5 How Common are Nonlinear Systems? / 11
2 Background 13
2.1 Vectors and Matrices / 13
2.2 Gaussian Random Variables / 14
2.2.1 Products of Gaussian Variables / 15
2.3 Correlation Functions / 16
v
Vi CONTENTS
2.3.1 Autocorrelation Functions / 16
2.3.2 Cross Correlation Functions / 18
2.3.3 Effects of Noise / 20
2.3.4 Estimates of Correlation Functions / 21
2.3.5 Frequency Domain Expressions / 22
2.3.6 Applications / 23
2.3.7 Higher Order Correlation Functions / 25
2.4 Mean Square Parameter Estimation / 25
2.4.1 Linear Least Squares Regression / 26
2.4.2 Properties of Estimates / 27
2.5 Polynomials / 29
2.5.1 Power Series / 29
2.5.2 Orthogonal Polynomials / 30
2.5.3 Hermite Polynomials / 31
2.5.4 Tchebyshev Polynomials / 32
2.5.5 Multiple Variable Polynomials / 33
2.6 Notes and References / 35
2.7 Problems / 36
2.8 Computer Exercises / 36
3 Models of Linear Systems 39
3.1 Linear Systems / 39
3.2 Nonparametric Models / 40
3.2.1 Time Domain Models / 41
3.2.2 Frequency Domain Models / 43
3.3 Parametric Models / 46
3.3.1 Parametric Frequency Domain Models / 46
3.3.2 Discrete Time Parametric Models / 48
3.4 State Space Models / 52
3.4.1 Example: Human Ankle Compliance—Discrete Time,
State Space Model / 54
3.5 Notes and References / 54
3.6 Theoretical Problems / 55
3.7 Computer Exercises / 56
4 Models of Nonlinear Systems 57
4.1 The Volterra Series / 57
4.1.1 The Finite Volterra Series / 59
4.1.2 Multiple Input Systems / 62
4.1.3 Polynomial Representation / 64
4.1.4 Convergence Issues(^) / 65
CONTENTS Vii
4.2 The Wiener Series / 67
4.2.1 Orthogonal Expansion of the Volterra Series / 68
4.2.2 Relation Between the Volterra and Wiener Series / 70
4.2.3 Example: Peripheral Auditory Model—Wiener Kernels / 71
4.2.4 Nonwhite Inputs / 73
4.3 Simple Block Structures / 73
4.3.1 The Wiener Model / 73
4.3.2 The Hammerstein Model / 77
4.3.3 Sandwich or Wiener Hammerstein Models / 79
4.3.4 NLN Cascades / 83
4.3.5 Multiple Input Multiple Output Block Structured Models / 87
4.4 Parallel Cascades / 87
4.4.1 Approximation Issues(^) / 89
4.5 The Wiener Bose Model / 91
4.5.1 Similarity Transformations and Uniqueness / 92
4.5.2 Approximation Issues(f) / 94
4.5.3 Volterra Kernels of the Wiener Bose Model / 94
4.5.4 Wiener Kernels of the Wiener Bose Model / 95
4.5.5 Relationship to the Parallel Cascade Model / 97
4.6 Notes and References / 100
4.7 Theoretical Problems / 100
4.8 Computer Exercises / 101
5 Identification of Linear Systems 103
5.1 Introduction / 103
5.1.1 Example: Identification of Human Joint Compliance / 103
5.1.2 Model Evaluation / 105
5.2 Nonparametric Time Domain Models / 107
5.2.1 Direct Estimation / 107
5.2.2 Least Squares Regression / 108
5.2.3 Correlation Based Methods / 109
5.3 Frequency Response Estimation / 115
5.3.1 Sinusoidal Frequency Response Testing / 115
5.3.2 Stochastic Frequency Response Testing / 116
5.3.3 Coherence Functions / 117
5.4 Parametric Methods / 119
5.4.1 Regression / 119
5.4.2 Instrumental Variables / 120
5.4.3 Nonlinear Optimization / 121
5.5 Notes and References / 122
5.6 Computer Exercises / 122
Viii CONTENTS
6 Correlation Based Methods 125
6.1 Methods for Functional Expansions / 125
6.1.1 Lee Schetzen Cross Correlation / 125
6.1.2 Colored Inputs / 140
6.1.3 Frequency Domain Approaches / 144
6.2 Block Structured Models / 149
6.2.1 Wiener Systems / 150
6.2.2 Hammerstein Models / 155
6.2.3 LNL Systems / 162
6.3 Problems / 167
6.4 Computer Exercises / 167
7 Explicit Least Squares Methods 169
7.1 Introduction / 169
7.2 The Orthogonal Algorithms / 169
7.2.1 The Orthogonal Algorithm / 171
7.2.2 The Fast Orthogonal Algorithm / 173
7.2.3 Variance of Kernel Estimates / 180
7.2.4 Example: Fast Orthogonal Algorithm Applied to Simulated Fly
Retina Data / 182
7.2.5 Application: Dynamics of the Cockroach Tactile Spine / 186
7.3 Expansion Bases / 187
7.3.1 The Basis Expansion Algorithm / 190
7.3.2 The Laguerre Expansion / 191
7.3.3 Limits on a I 192
7.3.4 Choice of a and P I 194
7.3.5 The Laguerre Expansion Technique / 195
7.3.6 Computational Requirements / 195
7.3.7 Variance of Laguerre Kernel Estimates / 195
7.3.8 Example: Laguerre Expansion Kernels of the Fly
Retina Model / 196
7.4 Principal Dynamic Modes / 198
7.4.1 Example: Principal Dynamic Modes of the Fly
Retina Model / 200
7.4.2 Application: Cockroach Tactile Spine / 201
7.5 Problems / 205
7.6 Computer Exercises / 205
8 Iterative Least Squares Methods 207
8.1 Optimization Methods / 207
8.1.1 Gradient Descent Methods / 208
CONTENTS ix
8.1.2 Identification of Block Structured Models / 209
8.1.3 Second Order Optimization Methods / 212
8.1.4 Jacobians for Other Block Structures / 216
8.1.5 Optimization Methods for Parallel Cascade Models / 219
8.1.6 Example: Using a Separable Volterra Network / 220
8.2 Parallel Cascade Methods / 223
8.2.1 Parameterization Issues / 226
8.2.2 Testing Paths for Significance / 228
8.2.3 Choosing the Linear Elements / 230
8.2.4 Parallel Wiener Cascade Algorithm / 242
8.2.5 Longer Cascades / 242
8.2.6 Example: Parallel Cascade Identification / 243
8.3 Application: Visual Processing in the Light Adapted Fly Retina / 246
8.4 Problems / 249
8.5 Computer Exercises / 250
References 251
Index 259
IEEE Press Series in Biomedical Engineering 262 |
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| author | Westwick, David T. Kearney, Robert E. 1947- |
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| author_facet | Westwick, David T. Kearney, Robert E. 1947- |
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| callnumber-subject | QP - Physiology |
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| dewey-full | 612/.01/5118 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 612 - Human physiology |
| dewey-raw | 612/.01/5118 |
| dewey-search | 612/.01/5118 |
| dewey-sort | 3612 11 45118 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| discipline_str_mv | Medizin Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
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| id | DE-604.BV021818341 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:53:15Z |
| indexdate | 2024-07-09T20:45:20Z |
| institution | BVB |
| isbn | 9780471274568 0471274569 |
| language | English |
| lccn | 2003043255 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015030526 |
| oclc_num | 51756004 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-83 DE-91G DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM DE-83 DE-91G DE-BY-TUM |
| physical | XII, 261 S. Ill. 25 cm |
| publishDate | 2003 |
| publishDateSearch | 2003 |
| publishDateSort | 2003 |
| publisher | IEEE Press Wiley-Interscience |
| record_format | marc |
| series2 | IEEE Press series on biomedical engineering |
| spelling | Westwick, David T. Verfasser aut Identification of nonlinear physiological systems David T. Westwick, Robert E. Kearney Piscataway, N.J. IEEE Press 2003 Hoboken, NJ Wiley-Interscience XII, 261 S. Ill. 25 cm txt rdacontent n rdamedia nc rdacarrier IEEE Press series on biomedical engineering "IEEE Engineering in Medicine and Biology Society, Sponsor." Includes bibliographical references (p. 251-257) and index Biomathematik swd Mathematisches Modell swd Nichtlineares System swd Physiologie swd aPhysiology xMathematical models aNonlinear systems Nichtlineares System (DE-588)4042110-7 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Physiologie (DE-588)4045981-0 gnd rswk-swf Physiologie (DE-588)4045981-0 s Systemidentifikation (DE-588)4121753-6 s Nichtlineares System (DE-588)4042110-7 s DE-604 Kearney, Robert E. 1947- Verfasser (DE-588)173651933 aut http://www.loc.gov/catdir/bios/wiley044/2003043255.html Contributor biographical information http://www.loc.gov/catdir/description/wiley037/2003043255.html Publisher description http://www.loc.gov/catdir/toc/wiley032/2003043255.html Table of contents HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015030526&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Westwick, David T. Kearney, Robert E. 1947- Identification of nonlinear physiological systems Biomathematik swd Mathematisches Modell swd Nichtlineares System swd Physiologie swd aPhysiology xMathematical models aNonlinear systems Nichtlineares System (DE-588)4042110-7 gnd Systemidentifikation (DE-588)4121753-6 gnd Physiologie (DE-588)4045981-0 gnd |
| subject_GND | (DE-588)4042110-7 (DE-588)4121753-6 (DE-588)4045981-0 |
| title | Identification of nonlinear physiological systems |
| title_auth | Identification of nonlinear physiological systems |
| title_exact_search | Identification of nonlinear physiological systems |
| title_exact_search_txtP | Identification of nonlinear physiological systems |
| title_full | Identification of nonlinear physiological systems David T. Westwick, Robert E. Kearney |
| title_fullStr | Identification of nonlinear physiological systems David T. Westwick, Robert E. Kearney |
| title_full_unstemmed | Identification of nonlinear physiological systems David T. Westwick, Robert E. Kearney |
| title_short | Identification of nonlinear physiological systems |
| title_sort | identification of nonlinear physiological systems |
| topic | Biomathematik swd Mathematisches Modell swd Nichtlineares System swd Physiologie swd aPhysiology xMathematical models aNonlinear systems Nichtlineares System (DE-588)4042110-7 gnd Systemidentifikation (DE-588)4121753-6 gnd Physiologie (DE-588)4045981-0 gnd |
| topic_facet | Biomathematik Mathematisches Modell Nichtlineares System Physiologie aPhysiology xMathematical models aNonlinear systems Systemidentifikation |
| url | http://www.loc.gov/catdir/bios/wiley044/2003043255.html http://www.loc.gov/catdir/description/wiley037/2003043255.html http://www.loc.gov/catdir/toc/wiley032/2003043255.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015030526&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT westwickdavidt identificationofnonlinearphysiologicalsystems AT kearneyroberte identificationofnonlinearphysiologicalsystems |