On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Kassel, Hessen
Kassel University Press
2022
|
| Series: | Schriftenreihe Mess- und Regelungstechnik der Universität Kassel
11 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XI, 166 Seiten 24 cm x 17 cm |
| ISBN: | 9783737610094 3737610096 |
Staff View
MARC
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| 245 | 1 | 0 | |a On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification |c David Leonardo Arengas Rojas |
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| 653 | |a information matrix | ||
| 653 | |a informative data | ||
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Record in the Search Index
| _version_ | 1804183421840261120 |
|---|---|
| adam_text | CONTENTS
1
INTRODUCTION
1
1.1
MOTIVATION
........................................................................................
1
1.2
STATE
OF
THE
ART
..............................................................................
4
1.3
CONTRIBUTIONS
..................................................................................
9
1.4
THESIS
OUTLINE
.................................................................................
10
1.5
PUBLICATIONS
.....................................................................................
11
2
CHANGE
DETECTION
13
2.1
STOCHASTIC
PROCESSES
........................................................................
13
2.2
PARAMETRIC
MODELING
OF
TIME
SERIES
...............................................
18
2.3
POWER
SPECTRAL
DENSITY
..................................................................
23
2.4
DETECTION
PROBLEM
...........................................................................
26
2.5
DISCUSSION
.......................................................................................
30
3
3.3
3.4
58
4
3.5
3.6
3.7
61
61
DISCRETE-TIME
SYSTEMS
..................................................................
PARAMETRIC
MODELS
...........................................................................
3.2.1
SINGLE-INPUT
SINGLE-OUTPUT
MODELS
.......................................
3.2.2
MULTI-INPUT
MULTI-OUTPUT
MODELS
.......................................
PARAMETER
ESTIMATION
.....................................................................
3.3.1
LEAST
SQUARES
METHOD
............................................................
3.3.2
INSTRUMENTAL
VARIABLE
METHODS
.............................................
3.3.3
PREDICTION
ERROR
METHODS
......................................................
RECURSIVE
PARAMETER
ESTIMATION
...................................................
3.4.1
RECURSIVE
QR
LEAST
SQUARES
METHOD
.................................
3.4.2
OVERDETERMINED
RECURSIVE
INSTRUMENTAL
VARIABLES
METHOD
.
THE
CUSUM
TEST
FOR
INPUT-OUTPUT
MODELS
.................................
PERSISTENCE
OF
EXCITATION
...............................................................
DISCUSSION
.......................................................................................
COMBINATION
OF
DATA
SETS
FOR
SYSTEM
IDENTIFICATION
4.1
DATA
MERGING
PROBLEM
...............................................
31
31
32
37
39
42
43
44
47
50
50
53
54
SYSTEM
IDENTIFICATION
OF
PARAMETRIC
MODELS
3.1
3.2
II
CONTENTS
4.2
WEIGHTED
IDENTIFICATION
METHODS
....................................................
63
4.3
LEAST
SQUARES
APPROACH
FOR
DATA
MERGING
.....................................
64
4.4
INSTRUMENTAL
VARIABLES
APPROACH
FOR
DATA
MERGING
.....................
65
4.5
MULTIPLE-COST
APPROACH
FOR
DATA
MERGING
......................................
66
4.6
DISCUSSION
.......................................................................................
67
5
DATA
SELECTION
METHOD
FOR
SYSTEM
IDENTIFICATION
(DS4SID)
69
5.1
IDENTIFYING
DATA
SETS
PREDOMINANTLY
AT
STEADY-STATE
..................
69
5.2
GENERAL
OVERVIEW
OF
DS4SID
..........................................................
76
5.3
DETERMINING
LOWER
INTERVAL
BOUNDS
..............................................
79
5.4
DETERMINING
UPPER
INTERVAL
BOUNDS
..............................................
82
5.5
COMBINATION
OF
INFORMATIVE
INTERVALS
..............................................
86
5.6
DS4SID
ALGORITHM
...........................................................................
87
5.7
CHOICE
OF
DESIGN
PARAMETERS
..........................................................
92
5.8
DISCUSSION
........................................................................................
93
6
CASE
STUDIES
97
6.1
PERFORMANCE
ASSESSMENT
...................................................................
97
6.1.1
DATA
REDUCTION
RATIO
.............................................................
97
6.1.2
GOODNESS
OF
FIT
......................................................................
98
6.2
SIMULATION
CASE
STUDY:
BINARY
DISTILLATION
COLUMN
..................
98
6.2.1
PROCESS
DESCRIPTION
................................................................
98
6.2.2
CONTROLLER
DESIGN
.....................................................................
101
6.2.3
PERFORMED
EXPERIMENTS
............................................................
103
6.2.4
RESULTS
AND
DISCUSSION
............................................................
108
6.3
INDUSTRY-ORIENTED
CASE
STUDY:
THE
PROCESS
UNIT
II
........................
ILL
6.3.1
PROCESS
DESCRIPTION
AND
CONTROL
SCHEME
..............................
ILL
6.3.2
NOISE
ANALYSIS
...........................................................................
116
6.3.3
PERFORMED
EXPERIMENTS
............................................................
118
6.3.4
RESULTS
AND
DISCUSSION
............................................................
120
6.4
DISCUSSION
..........................................................................................
123
7
CONCLUSIONS
AND
OUTLOOK
125
7.1
CONCLUSIONS
...........................................................................................
125
7.2
OUTLOOK
.................................................................................................
128
APPENDIX
135
A
EXPERIMENTS
IN
THE
PROCESS
UNIT
II
135
CONTENTS
III
B
EVALUATION
OF
MODEL
RESIDUALS
139
B.L
OUTPUT
FLOW
RATE
..............................................................................
139
B.2
LEVEL
OF
THE
FIRST
REACTOR
..................................................................
145
BIBLIOGRAPHY
147
|
| adam_txt |
CONTENTS
1
INTRODUCTION
1
1.1
MOTIVATION
.
1
1.2
STATE
OF
THE
ART
.
4
1.3
CONTRIBUTIONS
.
9
1.4
THESIS
OUTLINE
.
10
1.5
PUBLICATIONS
.
11
2
CHANGE
DETECTION
13
2.1
STOCHASTIC
PROCESSES
.
13
2.2
PARAMETRIC
MODELING
OF
TIME
SERIES
.
18
2.3
POWER
SPECTRAL
DENSITY
.
23
2.4
DETECTION
PROBLEM
.
26
2.5
DISCUSSION
.
30
3
3.3
3.4
58
4
3.5
3.6
3.7
61
61
DISCRETE-TIME
SYSTEMS
.
PARAMETRIC
MODELS
.
3.2.1
SINGLE-INPUT
SINGLE-OUTPUT
MODELS
.
3.2.2
MULTI-INPUT
MULTI-OUTPUT
MODELS
.
PARAMETER
ESTIMATION
.
3.3.1
LEAST
SQUARES
METHOD
.
3.3.2
INSTRUMENTAL
VARIABLE
METHODS
.
3.3.3
PREDICTION
ERROR
METHODS
.
RECURSIVE
PARAMETER
ESTIMATION
.
3.4.1
RECURSIVE
QR
LEAST
SQUARES
METHOD
.
3.4.2
OVERDETERMINED
RECURSIVE
INSTRUMENTAL
VARIABLES
METHOD
.
THE
CUSUM
TEST
FOR
INPUT-OUTPUT
MODELS
.
PERSISTENCE
OF
EXCITATION
.
DISCUSSION
.
COMBINATION
OF
DATA
SETS
FOR
SYSTEM
IDENTIFICATION
4.1
DATA
MERGING
PROBLEM
.
31
31
32
37
39
42
43
44
47
50
50
53
54
SYSTEM
IDENTIFICATION
OF
PARAMETRIC
MODELS
3.1
3.2
II
CONTENTS
4.2
WEIGHTED
IDENTIFICATION
METHODS
.
63
4.3
LEAST
SQUARES
APPROACH
FOR
DATA
MERGING
.
64
4.4
INSTRUMENTAL
VARIABLES
APPROACH
FOR
DATA
MERGING
.
65
4.5
MULTIPLE-COST
APPROACH
FOR
DATA
MERGING
.
66
4.6
DISCUSSION
.
67
5
DATA
SELECTION
METHOD
FOR
SYSTEM
IDENTIFICATION
(DS4SID)
69
5.1
IDENTIFYING
DATA
SETS
PREDOMINANTLY
AT
STEADY-STATE
.
69
5.2
GENERAL
OVERVIEW
OF
DS4SID
.
76
5.3
DETERMINING
LOWER
INTERVAL
BOUNDS
.
79
5.4
DETERMINING
UPPER
INTERVAL
BOUNDS
.
82
5.5
COMBINATION
OF
INFORMATIVE
INTERVALS
.
86
5.6
DS4SID
ALGORITHM
.
87
5.7
CHOICE
OF
DESIGN
PARAMETERS
.
92
5.8
DISCUSSION
.
93
6
CASE
STUDIES
97
6.1
PERFORMANCE
ASSESSMENT
.
97
6.1.1
DATA
REDUCTION
RATIO
.
97
6.1.2
GOODNESS
OF
FIT
.
98
6.2
SIMULATION
CASE
STUDY:
BINARY
DISTILLATION
COLUMN
.
98
6.2.1
PROCESS
DESCRIPTION
.
98
6.2.2
CONTROLLER
DESIGN
.
101
6.2.3
PERFORMED
EXPERIMENTS
.
103
6.2.4
RESULTS
AND
DISCUSSION
.
108
6.3
INDUSTRY-ORIENTED
CASE
STUDY:
THE
PROCESS
UNIT
II
.
ILL
6.3.1
PROCESS
DESCRIPTION
AND
CONTROL
SCHEME
.
ILL
6.3.2
NOISE
ANALYSIS
.
116
6.3.3
PERFORMED
EXPERIMENTS
.
118
6.3.4
RESULTS
AND
DISCUSSION
.
120
6.4
DISCUSSION
.
123
7
CONCLUSIONS
AND
OUTLOOK
125
7.1
CONCLUSIONS
.
125
7.2
OUTLOOK
.
128
APPENDIX
135
A
EXPERIMENTS
IN
THE
PROCESS
UNIT
II
135
CONTENTS
III
B
EVALUATION
OF
MODEL
RESIDUALS
139
B.L
OUTPUT
FLOW
RATE
.
139
B.2
LEVEL
OF
THE
FIRST
REACTOR
.
145
BIBLIOGRAPHY
147 |
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| id | DE-604.BV047854906 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:15:52Z |
| indexdate | 2024-07-10T09:23:11Z |
| institution | BVB |
| institution_GND | (DE-588)1066127425 |
| isbn | 9783737610094 3737610096 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033237669 |
| oclc_num | 1298893614 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | XI, 166 Seiten 24 cm x 17 cm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Kassel University Press |
| record_format | marc |
| series | Schriftenreihe Mess- und Regelungstechnik der Universität Kassel |
| series2 | Schriftenreihe Mess- und Regelungstechnik der Universität Kassel |
| spelling | Arengas Rojas, David Leonardo Verfasser (DE-588)1255212551 aut On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification David Leonardo Arengas Rojas 202202 Kassel, Hessen Kassel University Press 2022 XI, 166 Seiten 24 cm x 17 cm txt rdacontent n rdamedia nc rdacarrier Schriftenreihe Mess- und Regelungstechnik der Universität Kassel 11 Parameterschätzung (DE-588)4044614-1 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf linear system identification information matrix informative data DS4SID Datenanalyse (DE-588)4123037-1 s Parameterschätzung (DE-588)4044614-1 s Soft Computing (DE-588)4455833-8 s DE-604 kassel university press (DE-588)1066127425 pbl Schriftenreihe Mess- und Regelungstechnik der Universität Kassel 11 (DE-604)BV042727582 11 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033237669&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20220217 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Arengas Rojas, David Leonardo On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification Schriftenreihe Mess- und Regelungstechnik der Universität Kassel Parameterschätzung (DE-588)4044614-1 gnd Soft Computing (DE-588)4455833-8 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4044614-1 (DE-588)4455833-8 (DE-588)4123037-1 |
| title | On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification |
| title_auth | On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification |
| title_exact_search | On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification |
| title_exact_search_txtP | On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification |
| title_full | On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification David Leonardo Arengas Rojas |
| title_fullStr | On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification David Leonardo Arengas Rojas |
| title_full_unstemmed | On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification David Leonardo Arengas Rojas |
| title_short | On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification |
| title_sort | on the detection and selection of informative subsequences from large historical data records for linear system identification |
| topic | Parameterschätzung (DE-588)4044614-1 gnd Soft Computing (DE-588)4455833-8 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Parameterschätzung Soft Computing Datenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033237669&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV042727582 |
| work_keys_str_mv | AT arengasrojasdavidleonardo onthedetectionandselectionofinformativesubsequencesfromlargehistoricaldatarecordsforlinearsystemidentification AT kasseluniversitypress onthedetectionandselectionofinformativesubsequencesfromlargehistoricaldatarecordsforlinearsystemidentification |