Time Series Analysis, identification and adaptive filtering:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Malabar, Fla.
Krieger
1989
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| Edition: | 2. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | 420 S. |
| ISBN: | 0894643150 |
Staff View
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Record in the Search Index
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| adam_text | Titel: Time series analysis, identification and adaptive filtering
Autor: Graupe, Daniel
Jahr: 1989
Contents Preface to the Second Edition vii Preface ix Chapter 1 Introduction, Goal and Outline 1 1.1 Preliminary Comments, 1 1.2 Purpose and Scope, 3 1.3 Why Time Domain?, 6 1.4 Outline, 8 1.5 Suggested Course Structures, 13 1.6 References, 13 Chapter 2 Basic Concepts and Definitions in Stochastic Processes 15 2.1 Introduction, 15 2.2 Fundamental Definitions in Probability Theory, 15 2.3 Some Common Distributions, 23 2.4 Important Concepts in Time Series Analysis, 25 2.5 Some Basic Properties of Gaussian Processes, 36 2.6 General Difficulties in Continuous Stochastic Processes Theory, 37 2.7 The Wiener Process, 38 2.8 The Role of Stochastic Integration, 39 2.9 References, 40 xiii
xlv Contents Chapter 3 Fundamental Inequalities and Convergence Theorems 3.1 Fundamental Inequalities in Stochastic Processes Theory, 41 3.2 Fundamental Stochastic Convergence Theorems, 43 3.3 References, 53 Chapter 4 The Role of the Linear Model 4.1 Motivation for Using White Noise to Drive Linear Models, 55 4.2 Innovations and Pseudo Innovations Sequences, 60 4.3 ARMA Models for Nonstationary Situations, 61 4.4 References, 62 Chapter 5 Least Squares Parameter Identification of Time Series and Systems 5.1 General, 63 5.2 Least Squares Identification, 65 5.3 Sequential Least Squares Identification, 70 5.4 Lattice Least Squares and Sequential Lattice Least Squares Algorithms, 73 5.5 Forgetting Factors in Sequential Identification of Time Varying Processes, 85 5.6 Batch vs. Recursive Processing, 87 5.7 References, 88 Chapter 6 Convergence of Least Squares Identifiers 6.1 A Convergence Proof for Cases of Stable Signal Models, 91 6.2 A Convergence Proof for Cases of Unstable Signal Models, 98 6.3 Convergence Rate of LS Identifiers and Their Maximum Likelihood Property, 106 6.4 Discussion of Properties of Identifier’s Robustness to Round- Off Error and Conclusions, 109 6.5 References, 110 Chapter 7 Recursive Gradient Identification Methods 7.1 Introduction, 111 7.2 SA Algorithm Description, 112 7.3 Convergence of SA Identifiers, 113 7.4 A Fast Sequential Lattice Gradient Algorithm, 120 7.5 A Comparison of Gradient Lattice vs. LS Identifiers, 122 7.6 References, 124
Contents xv Chapter 8 Identification and Order Determination: MA/ARMA, Non-lnvertible, Feedback and Partly-Deterministic Structures 125 8.1 Derivation of ARMA Parameters and Orders From Pure AR Model Parameters, 125 8.2 Identification for Cases of Non-lnvertible Systems, 138 8.3 ARMA Models with Zeros on The Unit Circle, 143 8.4 Direct Identification of Pure MA Models, 147 8.5 Direct Identification of Mixed ARMA Models, 149 8.6 Merits of Pure AR Models vs. Pure MA or Directly-Identified ARMA Models, 150 8.7 Identification of Input-Output-Noise Models, 153 8.8 Identification of Stable and Unstable Closed Loop Structures, 155 8.9 Selection of Sampling Interval in System Modeling, 160 8.10 References, 162 Chapter 9 Identification of Time Series with Time-Varying Parameters 163 9.1 Slow and Fast Parameter Variations: Piece-Wise Stationarity, Forgetting Factors for Fast Nonstationarities, 163 9.2 Identification of Processes with Inherently Nonstationary Parameters (INSP) Based on Extended Kalman Filtering, 164 9.3 Properties of the Inherently Non-Stationary Parameter (INSP) Identifiers, 167 9.4 Comparative Performance Evaluation of INSP, Piece-Wise Parameter-Stationary and Forgetting-Factor-Based Identifiers, 167 9.5 Modified SLSFF Identifiers for Process with Fast Time-Varying Parameters, 176 9.6 References, 176 Chapter 10 Adaptive Decision Theory 179 10.1 Problem Statement, 179 10.2 Definitions, 180 10.3 Bayes Decision Rule—Binary Division, 181 10.4 Self-Adaptive Decision Algorithm, 184 10.5 Self-Adaptive Decisions: Multi-Class Situation, 188 10.6 The Role of Classification Algorithms in Detection and Pattern Recognition Problems, 188 10.7 Decision-Cell Approach to Adaptive Decisions with Reinforcement, 189 References, 192 10.8
xvi Contents Chapter 11 Optimal Linear Filtering of Noise 195 11.1 Problem Statement, 195 11.2 The Wiener Filter, 197 11.3 The Kalman Filter, 200 11.4 The Augmented Kalman Filter for Colored Measurement Noise, 205 11.5 Error Properties and Comparisons of Wiener and Kalman Filters, 207 11.6 References, 214 Chapter 12 Adaptive Filtering 217 12.1 Adaptive Filtering with Partial Knowledge, 217 _ 12.2 Adaptive Filtering with Unknown Parameters of Signal and of Noise, 221 12.3 References, 234 Chapter 13 Adaptive Control 237 13.1 General Problem Statement and Certainty Equivalence Considérations, 237 13.2 Adaptive Control via Separately Designed Identifier- Filter-Controller, 238 13.3 A Box-Jenkins-Type Adaptive Control Design, 249 13.4 Prédictive Adaptive Control, 251 13.5 References, 265 Appendix A Computer Printouts of Programs and Results 267 Program P-5.1 Program P-5.2 Program P-5.3 Program P-5.4 Program P-5.5 Program P-5.6 Program P-5.7 Program P-5.8 SLS Algorithm: Evaluation of Effect of P 0 on SLS Identification Error, 267 Evaluation of Effect of Initial Estimate on SLS Identification Error, 274 SLS Identification of Unstable Time Series Models, 278 Effect of P 0 on SLS Identification of Unstable Time Series, 280 SLS Identification Process With Poles on Unit Circle of fi-Plane, 284 SLS Identification Cases of Pole on Unit Circle With Other Pole Outside/Inside Unit Circle, 293 Sequential Least Squares Lattice Identification—Stable System, 303 Sequential Least Squares Lattice Identification—Unstable System, 314
Contents xvii Program P-7.1 Program P-7.2 Program P-8.1 Program P-9. 1 Program P-10.1 Program P-12.1 Gradient Lattice Identification—Stable System, 316 Gradient Lattice Identification of Unstable Systems, 325 SLS Identification of Non-Invertible Time Series Model, 326 Extended-Kalman-Filter-Based Inherently-Non-Stationary- Parameters (INSP) Identifier, 329 Self-Adaptive Classification, 332 Self-Adaptive Filter, 339 Appendix B Itoo Case Studies 345 B.l Multifunctional Prosthesis and Orthosis Control Via Time-Series Identification and Discrimination of Single-Site Myoelectric Signal Signatures, 345 B.2 EMG Signal Identification and Discrimination for Controlling Electrical Stimulation to Provide Paraplegics with Certain Walking Capabilities, 357 B.3 References, 361 Appendix C Some Fundamental Matrix Relations 363 C.l Norm of a Matrix, 363 C.2 Eigenvalues and Spectral Radius, 364 C.3 Determinants and Trace Functions, 365 C.4 Polynomial Relations and Non-Derogatory Matrices, 366 C.5 Positive/Negative (Semi) Definiteness, 366 C.6 Orthogonal and Symmetric Matrices, 367 C.7 Inverses, 368 C.8 Diagonalization, 369 C.9 Companion Form, 371 C. 10 Differentiation, 372 C.l 1 Some Other Matrix Properties, 373 C. 12 References, 374 Appendix D Transfer-Function/State-Space Transformations 375 D. 1 Transformation ffom State Space to Transfer Function, 375 D.2 Transformation from Transfer Function to State Space, 376 D. 3 References, 378 Appendix E Some Lemmas on Series Convergences 379 E. 1-E. 14 Lemmas, 379 E.15 References, 386 Problems 387 Authors Index 403 Subject Index 407
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| author | Graupe, Daniel |
| author_facet | Graupe, Daniel |
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| bvnumber | BV024673155 |
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| discipline | Mathematik |
| edition | 2. ed. |
| format | Book |
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| spelling | Graupe, Daniel Verfasser aut Time Series Analysis, identification and adaptive filtering Daniel Graupe 2. ed. Malabar, Fla. Krieger 1989 420 S. txt rdacontent n rdamedia nc rdacarrier Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihe (DE-588)4127298-5 gnd rswk-swf Adaptives Filter (DE-588)4141377-5 gnd rswk-swf 1\p (DE-588)4151278-9 Einführung gnd-content Zeitreihenanalyse (DE-588)4067486-1 s Adaptives Filter (DE-588)4141377-5 s DE-604 Zeitreihe (DE-588)4127298-5 s HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018084147&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Graupe, Daniel Time Series Analysis, identification and adaptive filtering Zeitreihenanalyse (DE-588)4067486-1 gnd Zeitreihe (DE-588)4127298-5 gnd Adaptives Filter (DE-588)4141377-5 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4127298-5 (DE-588)4141377-5 (DE-588)4151278-9 |
| title | Time Series Analysis, identification and adaptive filtering |
| title_auth | Time Series Analysis, identification and adaptive filtering |
| title_exact_search | Time Series Analysis, identification and adaptive filtering |
| title_full | Time Series Analysis, identification and adaptive filtering Daniel Graupe |
| title_fullStr | Time Series Analysis, identification and adaptive filtering Daniel Graupe |
| title_full_unstemmed | Time Series Analysis, identification and adaptive filtering Daniel Graupe |
| title_short | Time Series Analysis, identification and adaptive filtering |
| title_sort | time series analysis identification and adaptive filtering |
| topic | Zeitreihenanalyse (DE-588)4067486-1 gnd Zeitreihe (DE-588)4127298-5 gnd Adaptives Filter (DE-588)4141377-5 gnd |
| topic_facet | Zeitreihenanalyse Zeitreihe Adaptives Filter Einführung |
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