Verfügbarkeit: System identification with quantized observations

System identification with quantized observations:

Gespeichert in:

Bibliographische Detailangaben
Beteilige Person:	Wang, Le Yi (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Boston [u.a.] Birkhäuser 2010
Ausgabe:	1., Auflage
Schriftenreihe:	Systems & Control: Foundations & Applications
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020195180&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	XVIII, 317 S. graph. Darst.
ISBN:	9780817649555

Internformat

MARC


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Datensatz im Suchindex

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adam_text	IMAGE 1 CONTENTS PREFACE XIII CONVENTIONS XV GLOSSARY OF SYMBOLS XVII PART I: OVERVIEW 1 1 INTRODUCTION 3 1.1 MOTIVATING EXAMPLES . . . . . . . . . . . . . . . . . . . . . . 4 1.2 SYSTEM IDENTIFICATION WITH QUANTIZED OBSERVATIONS . . . . . 7 1.3 OUTLINE OF THE BOOK . . . . . . . . . . . . . . . . . . . . . . 8 2 SYSTEM SETTINGS 13 2.1 BASIC SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2 QUANTIZED OUTPUT OBSERVATIONS . . . . . . . . . . . . . . . . 16 2.3 INPUTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4 SYSTEM CONFIGURATIONS . . . . . . . . . . . . . . . . . . . . . 18 2.4.1 FILTERING AND FEEDBACK CONFIGURATIONS . . . . . . . . 19 2.4.2 SYSTEMS WITH COMMUNICATION CHANNELS . . . . . . . 19 2.5 UNCERTAINTIES . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.5.1 SYSTEM UNCERTAINTIES: UNMODELED DYNAMICS . . . . 20 2.5.2 SYSTEM UNCERTAINTIES: FUNCTION MISMATCH . . . . . . 21 2.5.3 SENSOR BIAS AND DRIFTS . . . . . . . . . . . . . . . . . 21 2.5.4 NOISE . . . . . . . . . . . . . . . . . . . . . . . . . . 21 IMAGE 2 VIII CONTENTS 2.5.5 UNKNOWN NOISE CHARACTERISTICS . . . . . . . . . . . . 22 2.5.6 COMMUNICATION CHANNEL UNCERTAINTIES . . . . . . . . 22 2.6 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 PART II: STOCHASTIC METHODS FOR LINEAR SYSTEMS 23 3 EMPIRICAL-MEASURE-BASED IDENTIFICATION 25 3.1 AN OVERVIEW OF EMPIRICAL-MEASURE-BASED IDENTIFICATION . . 26 3.2 EMPIRICAL MEASURES AND IDENTIFICATION ALGORITHMS . . . . . 29 3.3 STRONG CONVERGENCE . . . . . . . . . . . . . . . . . . . . . . 32 3.4 ASYMPTOTIC DISTRIBUTIONS . . . . . . . . . . . . . . . . . . . 34 3.5 MEAN-SQUARE CONVERGENCE . . . . . . . . . . . . . . . . . . . 37 3.6 CONVERGENCE UNDER DEPENDENT NOISE . . . . . . . . . . . . . 41 3.7 PROOFS OF TWO PROPOSITIONS . . . . . . . . . . . . . . . . . . 43 3.8 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4 ESTIMATION ERROR BOUNDS: INCLUDING UNMODELED DYNAMICS 49 4.1 WORST-CASE PROBABILISTIC ERRORS AND TIME COMPLEXITY . . . 50 4.2 UPPER BOUNDS ON ESTIMATION ERRORS AND TIME COMPLEXITY 50 4.3 LOWER BOUNDS ON ESTIMATION ERRORS . . . . . . . . . . . . . 53 4.4 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5 RATIONAL SYSTEMS 59 5.1 PRELIMINARIES . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2 ESTIMATION OF X K . . . . . . . . . . . . . . . . . . . . . . . . 60 5.3 ESTIMATION OF PARAMETER * . . . . . . . . . . . . . . . . . . . 62 5.3.1 PARAMETER IDENTIFIABILITY . . . . . . . . . . . . . . . . 62 5.3.2 IDENTIFICATION ALGORITHMS AND CONVERGENCE ANALYSIS 65 5.4 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6 QUANTIZED IDENTIFICATION AND ASYMPTOTIC EFFICIENCY 67 6.1 BASIC ALGORITHMS AND CONVERGENCE . . . . . . . . . . . . . . 68 6.2 QUASI-CONVEX COMBINATION ESTIMATORS (QCCE) . . . . . . 70 6.3 ALTERNATIVE COVARIANCE EXPRESSIONS OF OPTIMAL QCCES . . . 72 6.4 CRAM´ ER-RAO LOWER BOUNDS AND ASYMPTOTIC EFFICIENCY OF THE OPTIMAL QCCE . . . . . . . . . . . . . . . . . . . . . . . 75 6.5 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7 INPUT DESIGN FOR IDENTIFICATION IN CONNECTED SYSTEMS 81 7.1 INVARIANCE OF INPUT PERIODICITY AND RANK IN OPEN- AND CLOSED-LOOP CONFIGURATIONS . . . . . . . . . . . . . . . . . . . . . . 82 7.2 PERIODIC DITHERS . . . . . . . . . . . . . . . . . . . . . . . . 83 7.3 SUFFICIENT RICHNESS CONDITIONS UNDER INPUT NOISE . . . . . . 85 7.4 ACTUATOR NOISE . . . . . . . . . . . . . . . . . . . . . . . . . 88 7.5 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 IMAGE 3 CONTENTS IX 8 IDENTIFICATION OF SENSOR THRESHOLDS AND NOISE DISTRIBUTION FUNCTIONS 95 8.1 IDENTIFICATION OF UNKNOWN THRESHOLDS . . . . . . . . . . . . 95 8.1.1 SUFFICIENT RICHNESS CONDITIONS . . . . . . . . . . . . . 96 8.1.2 RECURSIVE ALGORITHMS . . . . . . . . . . . . . . . . . 99 8.2 PARAMETERIZED DISTRIBUTION FUNCTIONS . . . . . . . . . . . . 99 8.3 JOINT IDENTIFICATION PROBLEMS . . . . . . . . . . . . . . . . . 101 8.4 RICHNESS CONDITIONS FOR JOINT IDENTIFICATION . . . . . . . . . 101 8.5 ALGORITHMS FOR IDENTIFYING SYSTEM PARAMETERS AND DISTRIBUTION FUNCTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . 103 8.6 CONVERGENCE ANALYSIS . . . . . . . . . . . . . . . . . . . . . 105 8.7 RECURSIVE ALGORITHMS . . . . . . . . . . . . . . . . . . . . . . 106 8.7.1 RECURSIVE SCHEMES . . . . . . . . . . . . . . . . . . . 107 8.7.2 ASYMPTOTIC PROPERTIES OF RECURSIVE ALGORITHM (8.14) 108 8.8 ALGORITHM FLOWCHARTS . . . . . . . . . . . . . . . . . . . . . 111 8.9 ILLUSTRATIVE EXAMPLES . . . . . . . . . . . . . . . . . . . . . . 113 8.10 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 PART III: DETERMINISTIC METHODS FOR LINEAR SYSTEMS 117 9 WORST-CASE IDENTIFICATION 119 9.1 WORST-CASE UNCERTAINTY MEASURES . . . . . . . . . . . . . . 120 9.2 LOWER BOUNDS ON IDENTIFICATION ERRORS AND TIME COMPLEXITY 121 9.3 UPPER BOUNDS ON TIME COMPLEXITY . . . . . . . . . . . . . 124 9.4 IDENTIFICATION OF GAINS . . . . . . . . . . . . . . . . . . . . . 127 9.5 IDENTIFICATION USING COMBINED DETERMINISTIC AND STOCHASTIC METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 9.5.1 IDENTIFIABILITY CONDITIONS AND PROPERTIES UNDER DETERMINISTIC AND STOCHASTIC FRAMEWORKS . . . . . . . . 136 9.5.2 COMBINED DETERMINISTIC AND STOCHASTIC IDENTIFICATION METHODS . . . . . . . . . . . . . . . . . . . . . . 139 9.5.3 OPTIMAL INPUT DESIGN AND CONVERGENCE SPEED UNDER TYPICAL DISTRIBUTIONS . . . . . . . . . . . . . . . . . 141 9.6 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 10 WORST-CASE IDENTIFICATION USING QUANTIZED OBSERVATIONS 149 10.1 WORST-CASE IDENTIFICATION WITH QUANTIZED OBSERVATIONS . . . 150 10.2 INPUT DESIGN FOR PARAMETER DECOUPLING . . . . . . . . . . . 151 10.3 IDENTIFICATION OF SINGLE-PARAMETER SYSTEMS . . . . . . . . . . 153 10.3.1 GENERAL QUANTIZATION . . . . . . . . . . . . . . . . . 154 10.3.2 UNIFORM QUANTIZATION . . . . . . . . . . . . . . . . . 159 10.4 TIME COMPLEXITY . . . . . . . . . . . . . . . . . . . . . . . . 163 10.5 EXAMPLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 10.6 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 IMAGE 4 X CONTENTS PART IV: IDENTIFICATION OF NONLINEAR AND SWITCHING SYSTEMS 171 11 IDENTIFICATION OF WIENER SYSTEMS 173 11.1 WIENER SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . 174 11.2 BASIC INPUT DESIGN AND CORE IDENTIFICATION PROBLEMS . . . . 175 11.3 PROPERTIES OF INPUTS AND SYSTEMS . . . . . . . . . . . . . . . 177 11.4 IDENTIFICATION ALGORITHMS . . . . . . . . . . . . . . . . . . . 179 11.5 ASYMPTOTIC EFFICIENCY OF THE CORE IDENTIFICATION ALGORITHMS 184 11.6 RECURSIVE ALGORITHMS AND CONVERGENCE . . . . . . . . . . . . 188 11.7 EXAMPLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 11.8 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 12 IDENTIFICATION OF HAMMERSTEIN SYSTEMS 197 12.1 PROBLEM FORMULATION . . . . . . . . . . . . . . . . . . . . . . 198 12.2 INPUT DESIGN AND STRONG-FULL-RANK SIGNALS . . . . . . . . . 199 12.3 ESTIMATES OF * WITH INDIVIDUAL THRESHOLDS . . . . . . . . . . 202 12.4 QUASI-CONVEX COMBINATION ESTIMATORS OF * . . . . . . . . . 204 12.5 ESTIMATION OF SYSTEM PARAMETERS . . . . . . . . . . . . . . . 212 12.6 EXAMPLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 12.7 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 13 SYSTEMS WITH MARKOVIAN PARAMETERS 225 13.1 MARKOV SWITCHING SYSTEMS WITH BINARY OBSERVATIONS . . . . 227 13.2 WONHAM-TYPE FILTERS . . . . . . . . . . . . . . . . . . . . . 227 13.3 TRACKING: MEAN-SQUARE CRITERIA . . . . . . . . . . . . . . . . 229 13.4 TRACKING INFREQUENTLY SWITCHING SYSTEMS: MAP METHODS . 237 13.5 TRACKING FAST-SWITCHING SYSTEMS . . . . . . . . . . . . . . . 242 13.5.1 LONG-RUN AVERAGE BEHAVIOR . . . . . . . . . . . . . . 243 13.5.2 EMPIRICAL MEASURE-BASED ESTIMATORS . . . . . . . . . 245 13.5.3 ESTIMATION ERRORS ON EMPIRICAL MEASURES: UPPER AND LOWER BOUNDS . . . . . . . . . . . . . . . . . . . . . 249 13.6 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 PART V: COMPLEXITY ANALYSIS 253 14 COMPLEXITIES, THRESHOLD SELECTION, ADAPTATION 255 14.1 SPACE AND TIME COMPLEXITIES . . . . . . . . . . . . . . . . . 256 14.2 BINARY SENSOR THRESHOLD SELECTION AND INPUT DESIGN . . . . 259 14.3 WORST-CASE OPTIMAL THRESHOLD DESIGN . . . . . . . . . . . . 261 14.4 THRESHOLD ADAPTATION . . . . . . . . . . . . . . . . . . . . . 264 14.5 QUANTIZED SENSORS AND OPTIMAL RESOURCE ALLOCATION . . . . 267 14.6 DISCUSSIONS ON SPACE AND TIME COMPLEXITY . . . . . . . . . 271 14.7 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 15 IMPACT OF COMMUNICATION CHANNELS 275 15.1 IDENTIFICATION WITH COMMUNICATION CHANNELS . . . . . . . . 276 IMAGE 5 CONTENTS XI 15.2 MONOTONICITY OF FISHER INFORMATION . . . . . . . . . . . . . . 277 15.3 FISHER INFORMATION RATIO OF COMMUNICATION CHANNELS . . . 278 15.4 VECTOR-VALUED PARAMETERS . . . . . . . . . . . . . . . . . . . 280 15.5 RELATIONSHIP TO SHANNON S MUTUAL INFORMATION . . . . . . . 282 15.6 TRADEOFF BETWEEN TIME INFORMATION AND SPACE INFORMATION 283 15.7 INTERCONNECTIONS OF COMMUNICATION CHANNELS . . . . . . . . 284 15.8 NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 A BACKGROUND MATERIALS 287 A.1 MARTINGALES . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 A.2 MARKOV CHAINS . . . . . . . . . . . . . . . . . . . . . . . . . 290 A.3 WEAK CONVERGENCE . . . . . . . . . . . . . . . . . . . . . . . 299 A.4 MISCELLANY . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 REFERENCES 305 INDEX 315
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spellingShingle	Wang, Le Yi System identification with quantized observations
title	System identification with quantized observations
title_auth	System identification with quantized observations
title_exact_search	System identification with quantized observations
title_full	System identification with quantized observations Le Yi Wang ; G. George Yin ; Ji-Feng Zhang ; Yanlong Zhao
title_fullStr	System identification with quantized observations Le Yi Wang ; G. George Yin ; Ji-Feng Zhang ; Yanlong Zhao
title_full_unstemmed	System identification with quantized observations Le Yi Wang ; G. George Yin ; Ji-Feng Zhang ; Yanlong Zhao
title_short	System identification with quantized observations
title_sort	system identification with quantized observations
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020195180&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT wangleyi systemidentificationwithquantizedobservations

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