Evolutionary system identification: modern concepts and practical applications
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Linz
Trauner
2008
|
| Schriftenreihe: | Schriften der Johannes-Kepler-Universität Linz
Reihe C, Technik und Naturwissenschaften ; 59 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zsfassung in dt. u. engl. Sprache |
| Beschreibung: | VI, 396 S. Ill., graph. Darst. 21 cm |
| ISBN: | 9783854995692 |
Internformat
MARC
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| 001 | BV035598085 | ||
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| 005 | 20100104 | ||
| 007 | t | ||
| 008 | 090703s2008 ad|| m||| 00||| eng d | ||
| 020 | |a 9783854995692 |c kart. : EUR 22,50 ; CHF 38,90 |9 978-3-85499-569-2 | ||
| 024 | 3 | |a 9783854995692 | |
| 035 | |a (OCoLC)442621986 | ||
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| 100 | 1 | |a Winkler, Stephan |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Evolutionary system identification |b modern concepts and practical applications |c Stephan Winkler |
| 264 | 1 | |a Linz |b Trauner |c 2008 | |
| 300 | |a VI, 396 S. |b Ill., graph. Darst. |c 21 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Schriften der Johannes-Kepler-Universität Linz : Reihe C, Technik und Naturwissenschaften |v 59 | |
| 500 | |a Zsfassung in dt. u. engl. Sprache | ||
| 502 | |a Zugl.: Linz, Univ., Diss., 2008 | ||
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Systemidentifikation |0 (DE-588)4121753-6 |D s |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-017653186 | ||
Datensatz im Suchindex
| _version_ | 1804139263481085952 |
|---|---|
| adam_text | Contents
1
Introduction
1
1.1
Thesis Outline
............................... 1
1.2
Research Project Background
...................... 4
1 Theoretical Aspects
5
2
Evolutionary Computation
7
2.1
Evolutionary Computation
........................ 7
2.2
Genetic Algorithms
............................ 9
2.2.1
Darwin s Evolution Theory
.................... 9
2.2.2
Basics of Genetic Algorithms
.................. 9
2.2.3
Problem Representation
..................... 14
2.3
Evolution Strategies
........................... 15
3
Genetic Programming
21
3.1
Main Ideas and Historical Background
................. 22
3.2
Chromosome Representation
....................... 24
3.2.1
Hierarchical Labeled Structure Trees
.............. 25
3.2.2
Modular Genetic Programming
................. 32
3.2.3
Other Representations
...................... 33
3.3
Basic Steps of the GP Process
...................... 35
3.3.1
Preparatory Steps
......................... 35
3.3.2
Initialization
............................ 36
3.3.3
The Genetic Process: Breeding Populations of Programs
... 37
3.3.4
Process Termination and Results Designation
......... 39
3.4
Typical Applications of Genetic Programming
............. 41
3.4.1
Automated Learning of Multiplexer Functions
......... 41
3.4.2
The Artificial Ant
......................... 42
3.4.3
Symbolic Regression
....................... 44
3.4.4
Other GP Applications
...................... 48
3.5
GP
Schema
Theories
........................... 49
3.5.1
Program Component GP Schemata
............... 50
3.5.2
Rooted Tree GP Schema Theories
................ 52
3.5.3
Exact GP Schema Theory
.................... 54
3.5.4
Summary
............................. 59
3.6
Current GP Challenges and Research Areas
.............. 59
3.7
Conclusion
................................. 63
3.8
Bibliographic Remarks
.......................... 63
Enhanced Selection Concepts
65
4.1
Gender Specific Parents Selection
.................... 65
4.2
Offspring Selection
............................ 66
Parallel Genetic Algorithms
69
•5.1
Parallelization of Genetic Algorithms
.................. 69
5.1.1
Global Parallelization
....................... 70
5.1.2
Coarse-Grained Parallel GAs
................... 70
5.1.3
Fine-Grained Parallel GAs
.................... 71
5.1.4
Hybrid Parallel GAs
....................... 71
5.1.5
Migration
............................. 72
5.1.6
The SASEGASA
......................... 72
5.2
Parallel Genetic Programming
...................... 73
Data Based Modeling
75
6.1
Basics
................................... 75
6.2
An Example
................................ 78
6.3
The Basic Steps in System Identification
................ 85
6.4
Data Based Modeling Using Genetic Programming
.......... 87
6.5
Appendix: Fitting Polynomials to Data
................. 89
GP Based System Identification
91
7.1
Introduction
................................ 91
7.2
Problem Representation
......................... 93
7.2.1
The Data Base and Data Partitions
............... 93
7.2.2
Scaling and De-Scaling Basic Problem Data
.......... 94
7.2.3
Definition of Minimum and Maximum Time Offsets
...... 95
7.2.4
Metadata
............................. 96
7.3
The Functions and Terminals Basis
................... 97
7.3.1
Motivation, Introduction
..................... 97
7.3.2
Definition of the Evaluation of Terminals
............ 98
7.3.3
Definition of the Evaluation of Functions
............ 98
7.3.4
String Representations of Terminals and Functions
......100
7.3.5
Parameterization of Terminals and Functions
..........101
7.4
Solution Representation
.........................103
7.4.1
Representing Formulas by Structure Trees
...........103
7.4.2
Operators for Initializing and Manipulating Model Structures
105
7.5
Solution Evaluation
............................107
7.5.1
Standard Solution Evaluation Operators
............107
7.5.2
Combined Solution Evaluation
..................109
7.5.3
Adjusted Solution Evaluation
..................110
7.5.4
Runtime Consumption Considerations
.............
Ill
7.5.5
Early Stopping of Model Evaluation
...............112
8
Structure Identification Applications
115
8.1
Regression and Time Series Analysis
..................115
8.1.1
Regression
.............................115
8.1.2
Time Series Analysis
.......................116
8.1.3
Time Series Specific Evaluation
.................117
8.2
Classification
...............................118
8.2.1
Introduction
............................118
8.2.2
Real-Valued Classification Using Genetic Programming
.... 119
8.2.3
Analyzing Classifiers
.......................120
8.2.4
Classification Specific Evaluation in GP
.............127
9
Incorporation of A Priori Knowledge
133
9.1
Introduction: A Priori Knowledge and GP
...............133
9.2
Introduction of Partial Models into GP
.................134
10
Local Adaptation in GP
139
10.1
Parameter Optimization
.........................139
10.2
Pruning
..................................142
10.2.1
Basics and Method Parameters
.................142
10.2.2
Pruning a Structure Tree
.....................145
11
Similarity Measures for GP Solutions
151
11.1
Evaluation Based Similarity Measures
..................152
11.2
Structural Similarity Measures
......................153
12
Population Dynamics
161
12.1
Parents Analysis
.............................162
12.2
Variables Diversity
............................163
12.2.1
Frequency Based Relevance of Variables
............163
12.2.2
Impact
Based Relevance of Variables
.............. 164
12.2.3
Weighting of Variables Relevance Estimations
......... 167
12.2.4
Calculating the Relevance of Variables in Populations
..... 167
12.3
Functions and Terminals Diversity
.................... 167
12.3.1
Frequency Based Relevance of Functions and Terminals
.... 168
12.3.2
Impact Based Relevance of Functions and Terminals
..... 168
12.3.3
Calculating the Relevance of Functions and Terminals
..... 170
12.4
Genetic Diversity
............................. 171
12.4.1
In Single-Population GP
..................... 171
1.2.4.2
In Multi-Population GP
..................... 172
13
GP in Volatile Environments
173
13.1
On-Line GP Based System Identification
................173
13.2
Sliding Window Behavior in GP
.....................176
13.2.1
Basics
...............................176
13.2.2
Selection Pressure as Window Moving Trigger
.........177
II Empirical Studies
179
14
Time Series Analysis
181
14.1
Virtual Sensors for Diesel Engine Emissions
..............181
14.1.1
Designing Virtual Sensors for Nitric Oxides (NO*)
.......182
14.1.2
Designing Virtual Sensors for Particulate Emissions (Soot)
. . 187
14.2
NOX Data Sets Used for Further Tests
.................188
14.2.1
ΛΌ,
Data Set II
.........................190
14.2.2
NOX Data Set III
.........................191
14.3
Pressure Differences in a Tractor Gearbox
...............196
14.3.1
The Gearbox Data Set
......................196
14.3.2
Modeling Methods Used for Analyzing the Gearbox Data Set
. 196
14.3.3
Test Results
............................199
14.3.4
Conclusion
.............................205
15
Classification
207
15.1
Medical Data Analysis
..........................207
15.1.1
Benchmark Data Sets
.......................207
15.1.2
Solution Representation Using Hybrid Tree Structures
.... 209
15.1.3
Evaluation of Classification Models
...............209
15.1.4
Finding Appropriate Thresholds: Dynamic Range Selection
. . 211
15.1.5
First Results and Optimal Parameter Settings
.........212
15.1.6
Graphical Classifier Analysis
...................216
15.1.7
Classification
Methods
Applied
in Detailed Test Series
.... 219
15.1.8
Detailed Test Series Results
...................223
15.1.9
Conclusion
.............................230
15.2
Quality Pre-Assessinent in Steel Industry
................232
15.2.1
Introduction
............................232
15.2.2
Solution Structure
........................233
15.2.3
Empirical Results
.........................237
15.2.4
Discussion
.............................240
16
GP in Volatile Environments
243
16.1
Simulated On-Line Design of Virtual Sensors
..............243
16.2
Selection Pressure Based Sliding Window GP
.............240
16.2.1
Parameter Settings and Test Results
..............247
16.2.2
Discussion
.............................249
17
Population Dynamics
251
17.1
Genetic Propagation
...........................251
17.1.1
Test Setup
.............................251
17.1.2
Test Results
............................252
17.1.3
Summary
.............................255
17.1.4
Additional Tests Using Random Parents Selection
.......255
17.2
Variables Diversity
............................258
17.2.1
First Exemplary Results
.....................258
17.2.2
Detailed Analysis, Comparing Standard GP to Extended GP
. 263
17.3
Single Population Diversity Analysis
..................270
17.3.1
GP Test Strategies
........................270
17.3.2
Test Results
............................271
17.3.3
Conclusion
.............................278
17.4
Multi
Population Diversity Analysis
...................279
17.4.1
GP Test Strategies
........................279
17.4.2
Test Results
............................280
17.4.3
Discussion
.............................284
17.5
Comparison of Population Diversity Measures
.............285
17.5.1
Test Setup
.............................285
17.5.2
Test Results
............................286
17.5.3
Conclusion
.............................296
17.6
Code Bloat. Pruning, and Population Diversity
............297
17.6.1
Introduction
............................297
17.6.2
Test Strategies
..........................298
17.6.3
Test Results
............................300
17.6.4
Conclusion
.............................309
18
Incorporation
of
A Priori Knowledge
313
18.1
Physical
Knowledge
about the
Formation
of NC
...........313
18.2
Incorporation of Knowledge about
ΝΟΛ
.................315
18.2.1
Introduction of a New Variable for HFM*
............316
18.2.2
Seeding Stub Models for NO,,
..................316
18.2.3
Defining Terminals and a Basic Function for
ΝΟΧ
.......316
18.3
Test Strategies
..............................317
18.4
Test Results
................................319
18.4.1
Test Series I: Using no Additional Information
.........319
18.4.2
Test Series II: Using an Additional Variable
..........319
18.4.3
Test Series III and IV: Inducing Model Structures into GP
. . 320
18.4.4
Test Series V: Using an Enhanced Functional Basis
......323
18.5
Conclusion
.................................326
19
Results Stability
327
19.1
Introduction
................................327
19.2
Test Setup
.................................328
19.3
Test Results
................................329
19.4
Conclusion
.................................333
III Conclusion
335
20
Conclusion and Future Perspectives
337
IV Indices
341
Bibliography
343
List of Tables
375
List of Figures
381
List of Algorithms
387
Curriculum
Vitae
389
List of Publications
393
|
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| ctrlnum | (OCoLC)442621986 (DE-599)OBVAC07492374 |
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| format | Thesis Book |
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| genre_facet | Hochschulschrift |
| id | DE-604.BV035598085 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:41:18Z |
| institution | BVB |
| isbn | 9783854995692 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017653186 |
| oclc_num | 442621986 |
| open_access_boolean | |
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| owner_facet | DE-739 DE-N2 DE-355 DE-BY-UBR DE-83 DE-188 |
| physical | VI, 396 S. Ill., graph. Darst. 21 cm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
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| publisher | Trauner |
| record_format | marc |
| series | Schriften der Johannes-Kepler-Universität Linz |
| series2 | Schriften der Johannes-Kepler-Universität Linz : Reihe C, Technik und Naturwissenschaften |
| spelling | Winkler, Stephan Verfasser aut Evolutionary system identification modern concepts and practical applications Stephan Winkler Linz Trauner 2008 VI, 396 S. Ill., graph. Darst. 21 cm txt rdacontent n rdamedia nc rdacarrier Schriften der Johannes-Kepler-Universität Linz : Reihe C, Technik und Naturwissenschaften 59 Zsfassung in dt. u. engl. Sprache Zugl.: Linz, Univ., Diss., 2008 Data Mining (DE-588)4428654-5 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Genetische Programmierung (DE-588)4500172-8 gnd rswk-swf Heuristik (DE-588)4024772-7 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Genetische Programmierung (DE-588)4500172-8 s Systemidentifikation (DE-588)4121753-6 s Data Mining (DE-588)4428654-5 s Heuristik (DE-588)4024772-7 s DE-604 Schriften der Johannes-Kepler-Universität Linz Reihe C, Technik und Naturwissenschaften ; 59 (DE-604)BV009806089 59 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017653186&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Winkler, Stephan Evolutionary system identification modern concepts and practical applications Schriften der Johannes-Kepler-Universität Linz Data Mining (DE-588)4428654-5 gnd Systemidentifikation (DE-588)4121753-6 gnd Genetische Programmierung (DE-588)4500172-8 gnd Heuristik (DE-588)4024772-7 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4121753-6 (DE-588)4500172-8 (DE-588)4024772-7 (DE-588)4113937-9 |
| title | Evolutionary system identification modern concepts and practical applications |
| title_auth | Evolutionary system identification modern concepts and practical applications |
| title_exact_search | Evolutionary system identification modern concepts and practical applications |
| title_full | Evolutionary system identification modern concepts and practical applications Stephan Winkler |
| title_fullStr | Evolutionary system identification modern concepts and practical applications Stephan Winkler |
| title_full_unstemmed | Evolutionary system identification modern concepts and practical applications Stephan Winkler |
| title_short | Evolutionary system identification |
| title_sort | evolutionary system identification modern concepts and practical applications |
| title_sub | modern concepts and practical applications |
| topic | Data Mining (DE-588)4428654-5 gnd Systemidentifikation (DE-588)4121753-6 gnd Genetische Programmierung (DE-588)4500172-8 gnd Heuristik (DE-588)4024772-7 gnd |
| topic_facet | Data Mining Systemidentifikation Genetische Programmierung Heuristik Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017653186&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV009806089 |
| work_keys_str_mv | AT winklerstephan evolutionarysystemidentificationmodernconceptsandpracticalapplications |