Nonlinear system identification: input-output modeling approach 2 Nonlinear system structure identification
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht [u.a.]
Kluwer Academic
1999
|
| Schriftenreihe: | Mathematical modelling
7,2 |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | VIII, S. 399 - 800 Ill., graph. Darst. |
| ISBN: | 0792358570 |
Internformat
MARC
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| 100 | 1 | |a Haber, Robert |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Nonlinear system identification |b input-output modeling approach |n 2 |p Nonlinear system structure identification |c by Robert Haber and Laszlo Keviczky |
| 264 | 1 | |a Dordrecht [u.a.] |b Kluwer Academic |c 1999 | |
| 300 | |a VIII, S. 399 - 800 |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 1 | |a Mathematical modelling |v 7,2 | |
| 490 | 0 | |a Mathematical modelling |v ... | |
| 700 | 1 | |a Keviczky, Laszlo |e Verfasser |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804127655228866560 |
|---|---|
| adam_text | V
CONTENTS
VOLUME 2
NONLINEAR SYSTEM STRUCTURE IDENTIFICATION
4. Nonlinearity Test Methods 399
4.1 INTRODUCTION 399
4.2 TIME DOMAIN AND STEADY STATE TESTS 401
4.3 TEST BASED ON THE NORMALIZED VARIANCE OF THE
NOISE FREE OUTPUT SIGNAL AROUND THE OUTPUT
SIGNAL OF THE BEST FITTING LINEAR MODEL 404
4.4 AVERAGE VALUE TEST OF THE OUTPUT SIGNAL 410
4.5 GAUSSIAN DISTRIBUTION TEST OF THE OUTPUT SIGNAL 417
4.6 FREQUENCY METHOD 424
4.7 LINEAR SPECTRAL DENSITY METHOD 426
4.8 LINEAR CORRELATION METHOD 427
4.9 SECOND ORDER (NONLINEAR) CROSS CORRELATION
METHOD
4.10 SECOND ORDER (OUTPUT) AUTO CORRELATION METHOD 439
4.11 SECOND ORDER (OUTPUT) SPECTRAL DENSITY METHOD 450
4.12 DISPERSION METHOD 452
4.13 METHOD BASED ON THE COMPARISON OF THE
VARIANCES OF THE NOISE FREE MODEL OUTPUT SIGNALS
OF THE IDENTIFIED BEST LINEAR AND OF AN
ALTERNATIVE NONLINEAR MODEL 459
4.14 METHOD BASED ON THE IDENTIFICATION OF
ORTHOGONAL SUBSYSTEMS 462
4.15 METHOD BASED ON THE IDENTIFICATION OF THE
FIRST ORDER URYSON MODEL 467
4.16 METHOD BASED ON PARAMETER ESTIMATION AND
F TEST OF LINEAR AND SIMPLE NONLINEAR
STRUCTURES 472
4.17 METHOD BASED ON RESIDUAL ANALYSIS OF THE BEST
FITTING LINEAR MODEL 476
4.18 CONCLUSIONS 480
4.19 REFERENCES 482
5 . Structure Identification 484
5.1 INTRODUCTION 484
5.2 SEPARATION OF THE SYSTEM S RESPONSE TO PARALLEL
CHANNELS OF DIFFERENT NONLINEAR DEGREE 485
5.3 CASCADE STRUCTURE IDENTIFICATION FROM PARALLEL
CHANNELS OF DIFFERENT NONLINEAR DEGREE 497
5.4 STRUCTURE IDENTIFICATION OF SYSTEMS CONTAINING
ONLY ONE NONLINEAR STATIC ELEMENT BY THE
FREQUENCY METHOD 502
5.5 STRUCTURE IDENTIFICATION OF BLOCK ORIENTED
MODELS 506
5.5.1 Method based on the estimated extended Wiener
Hammerstein cascade model 506
5.5.2 Method based on the estimated Volterra kernels 519
vi Contents
5.5.3 Frequency method 534
5.5.4 Evaluation of pulse and step responses 541
5.6 STRUCTURE IDENTIFICATION OF SIMPLE WIENER
HAMMERSTEIN CASCADE MODEL 543
5.6.1 Correlation analysis 544
5.6.2 Frequency method 555
5.6.3 Evaluation of pulse and step responses 558
5.7 STRUCTURE IDENTIFICATION AND PARAMETER
ESTIMATION OF QUASI LINEAR MODELS HAVING SIGNAL
DEPENDENT PARAMETERS 561
5.7.1 Method using normal operating data 562
5.7.2 Evaluation of step responses 566
5.8 TWO STEP STRUCTURE IDENTIFICATION METHOD: BEST
INPUT OUTPUT MODEL APPROXIMATION FROM NORMAL
OPERATING DATA AND EVALUATION OF ITS STEP
RESPONSES 567
5.9 SELECTION OF THE MOST SIGNIFICANT MODEL
COMPONENTS OF MODELS LINEAR IN PARAMETERS 576
5.9.1 All possible regressions 576
5.9.2 Forward and backward regression 582
5.9.3 Stepwise regression 584
5.9.4 Regression analysis using orthogonal model components 590
5.9.5 Term clustering 599
5.9.6 Genetic algorithm 600
5.10 GROUP METHOD OF DATA HANDLING (GMDH) 600
5.11 REFERENCES 633
6. Model Validity Tests 639
6.1 INTRODUCTION 639
6.2 TIME SEQUENCE PLOT OF THE COMPUTED MODEL
OUTPUT SIGNAL 639
6.3 CHECKING THE MEAN VALUE OF THE RESIDUAL FOR
ZERO VALUE 645
6.4 CHI SQUARE TEST OF THE RESIDUALS 646
6.5 TIME SEQUENCE PLOT OF THE RESIDUALS 646
6.6 TIME SEQUENCE PLOT OF CERTAIN MEAN VALUES OF
THE DATA TO THE ACTUAL TIME POINT 648
6.7 PLOTTING OF THE HISTOGRAM OF THE RESIDUALS 649
6.8 NORMALITY TEST OF THE RESIDUALS 650
6.9 FREQUENCY DOMAIN ANALYSIS OF THE RESIDUALS 651
6.10 RUN TEST OF THE RANDOMNESS OF THE RESIDUALS 651
6.11 DURBIN WATSON TEST OF THE RESIDUALS 653
6.12 ANALYSIS OF THE AUTO CORRELATION FUNCTION OF
THE RESIDUALS 654
6.13 PLOT OF THE RESIDUALS AGAINST THE INPUT, OUTPUT
AND PREVIOUS RESIDUAL VALUES 656
6.14 COMPUTATION AND PLOT OF THE CONDITIONAL MEAN
VALUE OF THE RESIDUALS AGAINST THE INPUT, OUTPUT,
AND THE PREVIOUS RESIDUAL VALUES 657
6.15 ANALYSIS OF THE CROSS DISPERSIONAL FUNCTIONS OF
THE RESIDUALS AND THE INPUT, OUTPUT, AND PREVIOUS
Contents vii
RESIDUAL VALUES 661
6.16 ANALYSIS OF DIFFERENT CORRELATION FUNCTIONS OF
THE RESIDUALS 662
6.17 CHECKING THE ESTIMATED PARAMETERS FOR
SIGNIFICANCE 705
6.18 CHECKING THE IDENTIFIED MODEL FOR PHYSICAL
INTERPRETATION 709
6.19 REFERENCES 710
7. Case Studies on Identification of Real Processes 711
7.1 ELECTRICALLY EXCITED BIOLOGICAL MEMBRANE 711
7.1.1 Description of the process 711
7.1.2 Conclusions drawn from the measured records 712
7.1.3 Modeling by the simple Wiener model with signal dependent
time constant 713
7.1.4 Modeling by a quasi linear second order lag with signal
dependent gain and time constant 719
7.1.5 Conclusions 722
7.2 FERMENTATION PROCESS 724
7.2.1 Description of the process 724
7.2.2 Process identification and computation of the optimal
control signals 724
7.2.3 Conclusions 725
7.3 ELECTRICALLY HEATED HEAT EXCHANGER 726
7.3.1 Description of the plant 726
7.3.2 Tuning the uncertainty factor in the theoretically derived
model 729
7.3.3 Process identification by the grapho analytical method 731
7.3.4 Discrete time identification of a linear model 735
7.3.5 Discrete time identification of a two variable general
quadratic model being linear in the parameters 736
7.3.6 Discrete time identification by means of stepwise regression 737
7.3.7 Discrete time identification of the parameters of a
continuous time quasi linear model with signal dependent
parameters 740
7.3.8 Discrete time identification of the parameters of a discrete
time quasi linear model with signal dependent parameters 741
7.3.9 Discrete time identification of the parameters of the
theoretically derived model 742
7.3.10 Conclusions 743
7.4 STEAM HEATED HEAT EXCHANGER 745
7.4.1 Description of the plant 745
7.4.2 Process identification at changes in the steam flow 746
7.4.3 Conclusions 749
7.5 DISTILLATION COLUMN SEPARATING METHANOL WATER
MDCrURE 750
7.5.1 Description of the plant 750
7.5.2 Process identification at changes in the reflux flow 751
7.5.3 Process identification at changes in the distillate flow 753
7.5.4 Process identification at changes in the heating power 755
7.5.5 Conclusions 756
viii Contents
7.6 DISTILLATION COLUMN SEPARATING ETHANOL WATER
MIXTURE 756
7.6.1 Description of the plant 756
7.6.2 Process identification at step changes in the reflux rate 759
7.6.3 Process identification using pseudo random binary excitation
in the reflux flow 759
7.6.4 Process identification at changes in the heating power 767
7.6.5 Process identification at changes in the feed rate 768
7.6.6 Conclusions 769
7.7 FLOOD PROCESS OF RIVER CACHE 770
7.7.1 Description of the process 770
7.7.2 Process identification by Volterra series 770
7.7.3 Process identification by parametric models 772
7.7.4 Conclusions 773
7.8 FLOOD PROCESS OF RIVER SCHWARZA 773
7.8.1 Description of the process 773
7.8.2 Previous process identification results 774
7.8.3 Process identification by different nonlinear parametric
models 775
7.8.4 Conclusions 776
7.9 OPEN CIRCUIT CEMENT GRINDING MILL PILOT PLANT 777
7.9.1 Description of the plant 777
7.9.2 Process identification by the grapho analytical method 779
7.9.3 Process identification by the least squares method at every
input flow step separately 781
7.9.4 Conclusions drawn from the identification of the linear
models in different working points 781
7.9.5 Discrete time process identification of the global valid
nonlinear model 783
7.9.6 Conclusions 786
7.10 REFERENCES 786
Author Index 789
Subject Index 794
|
| any_adam_object | 1 |
| author | Haber, Robert Keviczky, Laszlo |
| author_facet | Haber, Robert Keviczky, Laszlo |
| author_role | aut aut |
| author_sort | Haber, Robert |
| author_variant | r h rh l k lk |
| building | Verbundindex |
| bvnumber | BV012961431 |
| ctrlnum | (OCoLC)247579603 (DE-599)BVBBV012961431 |
| dewey-full | 003.75 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems |
| dewey-raw | 003.75 |
| dewey-search | 003.75 |
| dewey-sort | 13.75 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV012961431 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:36:48Z |
| institution | BVB |
| isbn | 0792358570 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008827836 |
| oclc_num | 247579603 |
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| physical | VIII, S. 399 - 800 Ill., graph. Darst. |
| publishDate | 1999 |
| publishDateSearch | 1999 |
| publishDateSort | 1999 |
| publisher | Kluwer Academic |
| record_format | marc |
| series | Mathematical modelling |
| series2 | Mathematical modelling |
| spelling | Haber, Robert Verfasser aut Nonlinear system identification input-output modeling approach 2 Nonlinear system structure identification by Robert Haber and Laszlo Keviczky Dordrecht [u.a.] Kluwer Academic 1999 VIII, S. 399 - 800 Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Mathematical modelling 7,2 Mathematical modelling ... Keviczky, Laszlo Verfasser aut (DE-604)BV012961340 2 Mathematical modelling 7,2 (DE-604)BV011613239 7,2 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008827836&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Haber, Robert Keviczky, Laszlo Nonlinear system identification input-output modeling approach Mathematical modelling |
| title | Nonlinear system identification input-output modeling approach |
| title_auth | Nonlinear system identification input-output modeling approach |
| title_exact_search | Nonlinear system identification input-output modeling approach |
| title_full | Nonlinear system identification input-output modeling approach 2 Nonlinear system structure identification by Robert Haber and Laszlo Keviczky |
| title_fullStr | Nonlinear system identification input-output modeling approach 2 Nonlinear system structure identification by Robert Haber and Laszlo Keviczky |
| title_full_unstemmed | Nonlinear system identification input-output modeling approach 2 Nonlinear system structure identification by Robert Haber and Laszlo Keviczky |
| title_short | Nonlinear system identification |
| title_sort | nonlinear system identification input output modeling approach nonlinear system structure identification |
| title_sub | input-output modeling approach |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008827836&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV012961340 (DE-604)BV011613239 |
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