Regressor and structure selection : uses of ANOVA in system identification

206 Pages·2011·2.23 MB·English

by  Ingela Lind.

LinköpingStudiesinScienceandTechnology. Dissertations.
No.1012
Regressor and Structure Selection
Uses of ANOVA in System Identification
Ingela Lind
DepartmentofElectricalEngineering
Linköpingsuniversitet,SE–58183Linköping,Sweden
Linköping2006
Thecoverpictureisalittle-leaflinden,TILIAcordata,whichisnamed“lind”inSwedish.
RegressorandStructureSelection:UsesofANOVAinSystemIdentification
(cid:13)c 2006IngelaLind
[e-mail]
www.control.isy.liu.se
DivisionofAutomaticControl
DepartmentofElectricalEngineering
Linköpingsuniversitet
SE–58183Linköping
Sweden
ISBN91-85523-98-4 ISSN0345-7524
PrintedbyLiU-Tryck,Linköping,Sweden2006
ToMattias

Abstract
Identificationofnonlineardynamicalmodelsofablackboxnatureinvolvesbothstructure
decisions(i.e.,whichregressorstouseandtheselectionofaregressorfunction),andthe
estimation of the parameters involved. The typical approach in system identification is
oftenamixofallthesesteps,whichforexamplemeansthattheselectionofregressorsis
basedonthefitsthatisachievedfordifferentchoices. Alternativelyonecouldtheninter-
prettheregressorselectionasbasedonhypothesistests(F-tests)atacertainconfidence
level that depends on the data. It would in many cases be desirable to decide which re-
gressorstouse,independentlyoftheothersteps. Asurveyofregressorselectionmethods
usedforlinearregressionandnonlinearidentificationproblemsisgiven.
In this thesis we investigate what the well known method of analysis of variance
(ANOVA) can offer for this problem. System identification applications violate many
of the ideal conditions for which ANOVA was designed and we study how the method
performsundersuchnon-idealconditions.ItturnsoutthatANOVAgivesbetterandmore
homogeneousresultscomparedtoseveralotherregressorselectionmethods.Somepracti-
calaspectsarediscussed,especiallyhowtocategorisethedatasetfortheuseofANOVA,
andwhethertobalancethedatasetusedforstructureidentificationornot.
AnANOVA-basedmethod,TestofInteractionsusingLayoutforIntermixedANOVA
(TILIA),forregressorselectionintypicalsystemidentificationproblemswithmanycan-
didateregressorsisdevelopedandtestedwithgoodperformanceonavarietyofsimulated
andmeasureddatasets.
TypicalsystemidentificationapplicationsofANOVA,suchasguidingthechoiceof
linear terms in the regression vector and the choice of regime variables in local linear
models,areinvestigated.
ItisalsoshownthattheANOVAproblemcanberecastasanoptimisationproblem.
Twomodified,convexversionsoftheANOVAoptimisationproblemarethenproposed,
anditturnsoutthattheyarecloselyrelatedtothenn-garroteandwaveletshrinkagemeth-
ods, respectively. In the case of balanced data, it is also shown that the methods have a
niceorthogonalitypropertyinthesensethatdifferentgroupsofparameterscanbecom-
putedindependently.
v

Acknowledgments
Firstofall,IwouldliketothankmysupervisorprofessorLennartLjungforlettingmejoin
thenice,enthusiasticandambitiousresearchersintheAutomaticControlgroup,andfor
suggestingsuchaninterestingtopicforresearch. Hehasshownhonourablepatiencewith
delaysduetomaternalleaves,andalsobeenveryencouragingwhenneeded. Withouthis
excellentguidanceandsupportthisthesiswouldnotexist.
A, for me, important part of the work is teaching. I can sincerely say that without
the support of professor Svante Gunnarsson, I would not have considered starting on,
or continuing graduate studies. Ulla Salaneck, who somehow manages to keep track of
all practical and administrative details, is also worth a special thanks. Thank you for
maintainingsuchawelcomingatmosphere.
Ihavespentlotsoftimeworkingtogetherwith(oreatingincompanyof)JacobRoll
duringtheseyears. Hehasbeenandisagoodfriendaswellasworkingpartner. Thank
you. Iwouldalsoliketothankalltheotherpeoplepreviouslyorpresentlyinthegroup,
fortheircheerfulattitude,andfortheirunbelievableabilitytospawndetaileddiscussions
ofanythingbetweenheavenandearthduringthecoffeebreaks.
A number of people have been a great help during the thesis writing. I would like
to thank Gustaf Hendeby and Dr. Martin Enquist for providing the style files used, and
Gustaf also for all his help with LaTeX issues. Henrik Tidefelt has helped me with the
picturesintheIntroduction. Thefollowingpeople(inalfabeticalorder)havehelpedme
byproofreadingpartsofthethesis: DanielAnkelhed, MarcusGerdin, JanneHarju, Dr.
Jacob Roll, Dr. Thomas Schön and Johanna Wallén. They have given many insightful
comments,whichhaveimprovedtheworkconsiderably. Thankyouall.
ThisworkhasbeensupportedbytheSwedishResearchCouncil(VR)andbythegrad-
uateschoolECSEL(ExcellenceCenterinComputerScienceandSystemsEngineeringin
Linköping),whicharegratefullyacknowledged.
I also want to thank my extended family for their love and support. Special thanks
tomyparentsforalwaysencouragingmeandtrustingmyabilitytohandlethingsonmy
own,tomyhusbandMattiasforsharingeverythingandtryingtoboostmysometimeslow
selfconfidence,tomyparentsinlawformakingmefeelpartoftheirfamily,andfinally
tomydaughtersElsaandNoraforgivingperspectiveontheimportantthingsinlife.
Lasthere,butmostcentraltome,IwouldliketothankJesusChristforhisboundless
graceandlove.
vii

Contents
1 Introduction 1
1.1 SystemIdentification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 RegressorSelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 ModelTypeSelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 ParameterEstimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6 ThesisOutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Survey of Methods for Finding Significant Regressors in Nonlinear Regres-
sion 11
2.1 BackgroundinLinearRegression . . . . . . . . . . . . . . . . . . . . . 12
2.1.1 AllPossibleRegressions . . . . . . . . . . . . . . . . . . . . . . 12
2.1.2 StepwiseRegression . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.3 BackwardElimination . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.4 Non-NegativeGarrote . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.5 Lasso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.6 ISRR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1.7 LARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 NonlinearMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 ComparisonofMethods . . . . . . . . . . . . . . . . . . . . . . 16
2.2.2 ExhaustiveSearch . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.3 Non-ParametricFPE . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.4 StepwiseRegressionofNARMAXModelsusingERR . . . . . . 19
2.2.5 Bootstrap-BasedConfidenceIntervals . . . . . . . . . . . . . . . 20
2.2.6 (Partial)LagDependenceFunction . . . . . . . . . . . . . . . . 21
2.2.7 LocalConditionalMeanandANOVA . . . . . . . . . . . . . . . 22
2.2.8 LocalConditionalVariance. . . . . . . . . . . . . . . . . . . . . 22
ix
x Contents
2.2.9 FalseNearestNeighbours . . . . . . . . . . . . . . . . . . . . . 23
2.2.10 LipschitzQuotient . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.11 RankofLinearisedSystem . . . . . . . . . . . . . . . . . . . . . 24
2.2.12 MutualInformation . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.13 MARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.14 Supanova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 TheANOVAIdea 27
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1.1 OriginandUseofANOVA . . . . . . . . . . . . . . . . . . . . . 27
3.1.2 SamplingDistributions . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Two-WayAnalysisofVariance . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.2 ANOVATests. . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.3 ANOVATable . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.4 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3 RandomEffectsandMixedModels . . . . . . . . . . . . . . . . . . . . 34
3.4 SignificanceandPowerofANOVA. . . . . . . . . . . . . . . . . . . . . 36
3.5 UnbalancedDataSets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.5.1 ProportionalData . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.5.2 ApproximateMethods . . . . . . . . . . . . . . . . . . . . . . . 40
3.5.3 ExactMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4 DeterminetheStructureofNFIRmodels 43
4.1 ProblemDescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1.1 Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1.2 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 StructureIdentificationusingANOVA . . . . . . . . . . . . . . . . . . . 46
4.2.1 ANOVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2.2 ChecksofAssumptionsandCorrections . . . . . . . . . . . . . . 48
4.2.3 AnalysisoftheTestSystemswithContinuous-LevelInput . . . . 48
4.3 ValidationBasedExhaustiveSearchWithinANNModels . . . . . . . . . 58
4.4 RegressorSelectionusingtheGammaTest. . . . . . . . . . . . . . . . . 60
4.5 RegressorSelectionusingtheLipschitzMethod . . . . . . . . . . . . . . 61
4.6 RegressorSelectionusingStepwiseRegressionandERR . . . . . . . . . 61
4.7 TestResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.7.1 Fixed-LevelInputSignal . . . . . . . . . . . . . . . . . . . . . . 62
4.7.2 Continuous-LevelInputSignal . . . . . . . . . . . . . . . . . . . 65
4.7.3 CorrelatedInputSignal . . . . . . . . . . . . . . . . . . . . . . . 67
4.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5 PracticalConsiderationswiththeUseofANOVA 73
5.1 WhichVariantofANOVAShouldbeUsed? . . . . . . . . . . . . . . . . 73
5.2 Categorisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2.1 IndependentRegressors . . . . . . . . . . . . . . . . . . . . . . 75
5.2.2 CorrelatedRegressors . . . . . . . . . . . . . . . . . . . . . . . 75