MARC details
| 000 -LEADER |
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| fixed length control field |
07385cam a22003494i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
11988817 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20201231162704.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
000428s2000 enka b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
0471986291 (acidfree paper) |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Description conventions |
rda |
| Transcribing agency |
DLC |
| Modifying agency |
DLC |
| Language of cataloging |
eng |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
621.3822 |
| Edition number |
21 |
| Item number |
N.M.I |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Niedźwiecki, Maciej. |
| 9 (RLIN) |
9885 |
| 245 10 - TITLE STATEMENT |
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| Title |
Identification of time-varying processes / |
| Statement of responsibility, etc |
Maciej Niedźwiecki. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Chichester ; |
| -- |
New York : |
| Name of publisher, distributor, etc |
Wiley, |
| Date of publication, distribution, etc |
[2000] |
| 264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Date of publication, distribution, etc |
©2000 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiii, 324 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
26 cm. |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
| Media type code |
n |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Carrier type code |
nc |
| Source |
rdacarrier |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references (pages [309]-320) and index. |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
1 Modeling Essentials 1 --<br/>1.1 Physical and instrumental approaches to modeling 1 --<br/>1.2 Titius--Bode law and the method of least sqares 7 --<br/>1.3 Principle of parsimony 8 --<br/>1.4 Mathematical models of stationary processes 9 --<br/>1.4.1 Autoregressive model 10 --<br/>1.4.2 Moving average model 18 --<br/>1.4.3 Equivalence of autoregressive and moving average models 24 --<br/>1.4.4 Mixed autoregressive moving average model 27 --<br/>1.4.5 A bridge to continuous-time processes 28 --<br/>1.4.6 Models with exogenous inputs 31 --<br/>1.4.7 Shorthand notation 31 --<br/>1.5 Model-based approach to adaptive signal processing and control 32 --<br/>1.5.1 Prediction 33 --<br/>1.5.2 Predictive coding of signals 34 --<br/>1.5.3 Detection and elimination of outliers 36 --<br/>1.5.4 Equalization of communication channels 40 --<br/>1.5.5 Spectrum estimation 42 --<br/>1.5.6 Adaptive control 47 --<br/>2 Models of Nonstationary Processes 51 --<br/>2.1 Origins of time dependence 51 --<br/>2.2 Characteristics of nonstationary processes 52 --<br/>2.3 Irreducible nonstationary processes and parameter tracking 55 --<br/>2.4 Measures of tracking ability 56 --<br/>2.5 Prior knowledge in identification of nonstationary processes 60 --<br/>2.5.1 Events and auxiliary measurements 61 --<br/>2.5.2 Probabilistic models 61 --<br/>2.5.3 Deterministic models 63 --<br/>2.6 Slowly varying systems and the concept of local stationarity 64 --<br/>2.7 Rate of process time variation 66 --<br/>2.7.1 Speed of variation and sampling frequency 66 --<br/>2.7.2 Nonstationarity degree 67 --<br/>2.8 Assumptions 70 --<br/>2.8.1 Dependence among regressors 70 --<br/>2.8.2 Dependence between system variables 71 --<br/>2.8.3 Persistence of excitation 71 --<br/>2.8.4 Boundedness of system variables 72 --<br/>2.8.5 Variation of system parameters 73 --<br/>2.9 About computer simulations 74 --<br/>3 Process Segmentation 79 --<br/>3.1 Nonadaptive segmentation 79 --<br/>3.1.1 Conditions of identifiability 80 --<br/>3.1.2 Recursive least squares algorithm 82 --<br/>3.2 Adaptive segmentation 86 --<br/>3.2.1 Segmentation based on the Akaike criterion 86 --<br/>3.2.2 Segmentation based on the generalized likelihood ratio test 93 --<br/>3.3 Extension to ARMAX processes 95 --<br/>3.3.1 Iterative estimation algorithms 95 --<br/>3.3.2 Recursive estimation algorithms 98 --<br/>3.3.3 Conditions of identifiability 100 --<br/>3.3.4 Adaptive segmentation 100 --<br/>4 Weighted Least Squares 103 --<br/>4.1 Estimation principles 103 --<br/>4.2 Estimation windows 104 --<br/>4.3 Static characteristics of WLS estimators 105 --<br/>4.3.1 Effective window width 106 --<br/>4.3.2 Equivalent window width 106 --<br/>4.3.3 Degree of window concentration 108 --<br/>4.4 Dynamic time-domain characteristics of WLS estimators 108 --<br/>4.4.1 Impulse response associated with WLS estimators 109 --<br/>4.4.2 Variability of WLS estimators 111 --<br/>4.5 Dynamic frequency-domain characteristics of WLS estimators 112 --<br/>4.5.1 Frequency characteristics associated with WLS estimators 112 --<br/>4.5.2 Properties of associated frequency characteristics 113 --<br/>4.5.3 Estimation delay of WLS estimators 115 --<br/>4.5.4 Matching characteristics of WLS estimators 117 --<br/>4.6 Principle of uncertainty 118 --<br/>4.7 Comparison of the EWLS and SWLS approaches 119 --<br/>4.8 Technical issues 122 --<br/>4.9 Computer simulations 125 --<br/>4.10 Extension to ARMAX processes 136 --<br/>5 Least Mean Squares 139 --<br/>5.1 Estimation principles 139 --<br/>5.2 Convergence and stability of LMS algorithms 141 --<br/>5.2.1 Analysis for independent regressors 143 --<br/>5.2.2 Analysis for dependent regressors 146 --<br/>5.3 Static characteristics of LMS estimators 148 --<br/>5.3.1 Equivalent memory of LMS estimators 149 --<br/>5.3.2 Normalized LMS estimators 153 --<br/>5.4 Dynamic characteristics of LMS estimators 154 --<br/>5.4.1 Impulse response associated with LMS estimators 154 --<br/>5.4.2 Frequency response associated with LMS estimators 155 --<br/>5.5 Comparison of the EWLS and LMS. estimators 156 --<br/>5.5.1 Initial convergence 156 --<br/>5.5.2 Tracking performance 159 --<br/>5.6 Computer simulations 166 --<br/>5.7 Extension to ARMAX processes 177 --<br/> |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
6.1 Approach based on process segmentation 179 --<br/>6.1.1 Estimation principles 179 --<br/>6.1.2 Invariance under the change of coordinates 183 --<br/>6.1.3 Static characteristics of BF estimators 186 --<br/>6.1.4 Dynamic characteristics of BF estimators 188 --<br/>6.1.5 Impulse response associated with BF estimators 190 --<br/>6.1.6 Frequency response associated with BF estimators 191 --<br/>6.1.7 Properties of the associated frequency characteristics 193 --<br/>6.1.8 Comparing the matching properties of different BF estimators 196 --<br/>6.2 Weighted basis function estimation 199 --<br/>6.2.1 Estimation principles 199 --<br/>6.2.2 Recursive WBF estimators 203 --<br/>6.2.3 Static characteristics of WBF estimators 205 --<br/>6.2.4 Impulse response associated with WBF estimators 209 --<br/>6.2.5 Frequency response associated with WBF estimators 210 --<br/>6.3 Computer simulations 215 --<br/>6.4 Method of basis functions: good news or bad news? 215 --<br/>7 Kalman Filtering 229 --<br/>7.1 Estimation principles 229 --<br/>7.2 Estimation based on the random walk model 231 --<br/>7.3 Estimation based on the integrated random walk models 234 --<br/>7.4 Stability and convergence of the RWKF algorithm 236 --<br/>7.5 Estimation memory of the RWKF algorithm 237 --<br/>7.6 Dynamic characteristics of RWKF estimators 242 --<br/>7.6.1 Impulse response associated with RWKF estimators 242 --<br/>7.6.2 Frequency response associated with RWKF estimators 243 --<br/>7.7 Convergence and tracking performance of RWKF estimators 244 --<br/>7.7.1 Initial convergence 244 --<br/>7.7.2 Tracking performance 244 --<br/>7.8 Parameter matching using the Kalman smoothing approach 247 --<br/>7.8.1 Fixed interval smoothing 248 --<br/>7.8.2 Fixed lag smoothing 249 --<br/>7.9 Computer simulations 250 --<br/>7.10 Extension to ARMAX processes 261 --<br/>8 Practical Issues 265 --<br/>8.1 Numerical safeguards 265 --<br/>8.1.1 Least squares algorithms 265 --<br/>8.1.2 Gradient algorithms 281 --<br/>8.1.3 Kalman filter algorithms 284 --<br/>8.2 Optimization 287 --<br/>8.2.1 Memory optimization 287 --<br/>8.2.2 Other optimization issues 297. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Adaptive signal processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Signal processing |
| General subdivision |
Digital techniques. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Time-series analysis. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
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| Materials specified |
Publisher description |
| Uniform Resource Identifier |
<a href="http://www.loc.gov/catdir/description/wiley036/00033019.html">http://www.loc.gov/catdir/description/wiley036/00033019.html</a> |
| 856 4# - ELECTRONIC LOCATION AND ACCESS |
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| Materials specified |
Table of Contents |
| Uniform Resource Identifier |
<a href="http://www.loc.gov/catdir/toc/onix07/00033019.html">http://www.loc.gov/catdir/toc/onix07/00033019.html</a> |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| a |
7 |
| b |
cbc |
| c |
orignew |
| d |
1 |
| e |
ocip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |