Soft Computing: Techniques in Engineering Sciences

230 Pages·2020·1.697 MB·English

by  Mangey Ram, Suraj B. Singh

MangeyRamandSurajB.Singh(Eds.)
SoftComputing
De Gruyter Series on the
Applications of Mathematics
in Engineering and
Information Sciences
Edited by
Mangey Ram
Volume 1
Soft Computing
Techniques in Engineering Sciences
Edited by
Mangey Ram and Suraj B. Singh
An electronic version of this book is freely available, thanks to the support of
libraries working with Knowledge Unlatched. KU is a collaborative initiative
designed to make high quality books Open Access. More information about the
initiative can be found at www.knowledgeunlatched.org
Editors
MangeyRam
GraphicEraDeemedtobeUniversity
DepartmentofMathematics,ComputerScienceandEngineering
566/6BellRoad
248002ClementTown,Dehradun,Uttarakhand,India
[email protected]
SurajB.Singh
G.B.PantUniversityofAgricultureandTechnology
263153UdhamSinghNagar,Pantnagar,Uttarakhand,India
[email protected]
ISBN978-3-11-062560-8
e-ISBN(PDF)978-3-11-062861-6
e-ISBN(EPUB)978-3-11-062571-4
ISSN2626-5427
DOIhttps://doi.org/10.1515/9783110628616
ThisworkislicensedunderaCreativeCommonsAttribution-NonCommercial-NoDerivatives4.0
InternationalLicense.Fordetailsgotohttp://creativecommons.org/licenses/by-nc-nd/4.0/.
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©2020MangeyRamandSurajB.Singh,publishedbyWalterdeGruyterGmbH,Berlin/Boston
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Preface
Conventionalcomputingorhardcomputingoftendependsonapreciselystated
analyticalmodelandmanytimesinvolvesalotofcomplexityduringcomputation
alongwithhighcomputationtime.Nowadays,therearealotofreal-worldengi-
neeringproblems,whichcannotbestudiedpreciselyduetothepresenceofim-
precisenessanduncertainties.Totacklethesetypesofproblems,softcomputing
techniqueshavefoundwideapplicationsandhavebeenprovedtobeapowerful
problem-solvingmethodologybecauseoftheirstronglearningandcognitiveabil-
ityandgoodtoleranceofuncertaintyandimprecision.Basically,theyareanex-
tensionofheuristicsandhelpinsolvingtypicalproblemsthatarehardtomodel
mathematically.Moreover,theyareeasytoaccommodatewithchangedscenario
andcanbeexecutedwithparallelcomputing.Needlesstosay,softcomputing
techniquesareanemergingapproach,whichincludestechniquessuchasfuzzy
logic,evolutionarycomputing,artificialneuralnetwork,andappliedstatistics.
Aninterestingfacttobenoteddownisthattheyareactuallydistinctappliedtech-
niquestosolveawiderangeofproblemsbutwhenappliedincollaborationhelpalot
insolvingcomplicatedproblemseasilyorrelativelyeasily.Theyhavewideapplica-
tionsinfieldssuchasmachineintelligence,computervision,VLSIdesign,medical
diagnosis,patternrecognition,networkoptimization,andweatherforecasting.
Keepingaforementionedfactsinview,wehavetriedtoeditabookcomprising
someofthecontemporaryresearchesindifferentreal-worldproblemswheresoft
computingtechniqueshavebeenapplied.Thebookconsistsoftenchaptersin
whichthefollowingstudiesareconducted:
– Chapter1focusesontheapplicationofneuralnetworkmodelsforfreezingof
gaitdetectionandpredictioninParkinson’sdiseaseinwhichauthorsdevelop
auser-independentfreezingofgaitdetectionandpredictionmodelthatwould
goalongwithnonpharmacologicaltreatments.
– Chapter2examinesanewfuzzydenovoprogrammingapproachforoptimal
systemdesign,whichproposesanewapproachforsolutionsofdenovopro-
grammingproblemsinfuzzyenvironment.Thisapproachisbuiltontheap-
proachbyLiandLee(1993),whichusespositiveandnegativeidealsolutions.
– Chapter3discussesaprobabilisticbilevelprogramminginStackelberggame
underfuzzyenvironmentinwhichresearchersdevelopacomputationalalgo-
rithmtosolveastochasticbilevelprogramminginStackelberggameusingfuzzy
optimizationtechnique.
– Chapter4studiesintuitionisticfuzzytrigonometricdistanceandsimilaritymea-
sureandtheirproperties.Inthischapter,theconceptofintuitionisticfuzzysets
isintroduced.Similarityanddistancemeasuresbetweenintuitionisticfuzzysets
arealsoexplainedandextendedthesemeasurestointuitionisticfuzzysets.
Somenewtrigonometricsimilarityanddistancemeasuresofintuitionisticfuzzy
OpenAccess.©2020MangeyRamandSurajB.Singh,publishedbyDeGruyter. Thisworkis
licensedunderaCreativeCommonsAttribution-NonCommercial-NoDerivatives4.0InternationalLicense.
https://doi.org/10.1515/9783110628616-202
VI Preface
setsarestudiedanditisshownthatanintuitionisticfuzzydistancemeasuresat-
isfiestherequiredidentitiesofintuitionisticfuzzysimilaritymeasures.
– Chapter5developsamathematicalmodelthroughtransmutationofactivation
energyfunction.Thisworkincorporatesthedifferentperspectiveofmodeling
ofactivationenergiesthroughingressionofanadditionalparameterofdistri-
butionfunctiontoincreasetheflexibilityandcontrollingabilityofmodelingby
thelinearmixing.
– Chapter6addressesforecastingofairqualityparametersusingsoftcomputing
techniques.Inthischapter,authorsstudiedtheapplicationofsoftcomputing
techniquesofartificialneuralnetworksandgeneticprogrammingforforecast-
ingofairqualityparametersafewtimestepsinadvance.
– Chapter7examinesarithmeticoperationsongeneralizedsemiellipticintuitionis-
ticfuzzynumbers(IFNs)anditsapplicationinmulticriteriadecisionmaking.In
thischapter,authorsdiscussthegeneralizedsemiellipticIFNandperformedits
arithmeticoperationswiththehelpof(α,β)cutmethod.Inthisstudy,theycom-
paredtheproposedIFNwithnormaltriangular,trapezoidal,semiellipticIFNs.
– Chapter8discussesthemethodforsolvingintuitionisticfuzzyassignment
problem.Inthisresearch,authorsuseanewlyproposedcentroidconceptrank-
ingmethodforintuitionisticfuzzynumbersandsolveassignmentproblem
whereassignmentcostsaretakenastriangularintuitionisticfuzzynumbers.
– Chapter9focusesonoptimizationofelectricdischargemachiningprocessthrough
evolutionarycomputingandfuzzymulticriteriadecision-makingtechniques.This
studyintendedtoexplorethebestpossiblesetofprocessparameters,namely,cur-
rent,voltage,andpulseontimeduringelectricdischargemachiningprocessin
ordertodeterminethechangesinperformancecharacteristicslikematerialre-
movalrateandelectrodewearrateformachiningofhighcarbonhighchromium
diesteelworkpiecebyapplyingthetitaniumnitride–coatedcopperelectrode.
– Chapter10studiesthefuzzyreliabilityofsystemsusingdifferenttypesoflevel
(λ,1)interval-valuedfuzzynumbers.Inthischapter,authorsobtainthefuzzy
reliabilityofseries,parallel,parallel–series,andseries–parallelsystemsas-
sumingthatallcomponentsofthesystemfollowdifferenttypesoflevel(λ,1)
interval-valuedfuzzynumbers(bothtriangularandtrapezoidal).
Thebookwillbeusefultoscientists,engineers,managers,seniorandpostgraduate
students,aswellasresearchscholars.
Acknowledgments
Itiswellknownthatbesidestheeditors,manyindividualshaveputmuchtimeand
energyintothebook.Firstandforemost,wewouldliketoexpressourgratitudeto
chaptercontributorsandreviewersfortheirtimelyefforts.Wewouldliketoextend
Preface VII
extraspecialthanksandappreciationtotheeditorial,production,andmarketing
teamatDeGruyterwhohelpedinbringingtheprojectinthefinalshape.
MangeyRam,
GraphicEraDeemedtobeUniversity,
Dehradun,India
S.B.Singh,
G.B.PantUniversityofAgriculture&Technology,
Pantnagar,India

Contents
Preface V
Abouttheeditors XI
Listofcontributors XIII
BharatendraRai,AmrutaMeshram
1 Applicationofneuralnetworktodetectfreezingofgaitinpatientswith
Parkinson’sdisease 1
NurullahUmarusman
2 Anewfuzzydenovoprogrammingapproachforoptimalsystem
design 13
SumitKumarMaiti,SankarKumarRoy
3 Aprobabilistictwo-levelprogramminginnoncooperativegameunder
fuzzyenvironment:comparativestudy 33
D.K.Sharma,RajneeTripathi
4 Intuitionisticfuzzytrigonometricdistanceandsimilaritymeasureand
theirproperties 53
AlokDhaundiyal,SurajB.Singh
5 DistributedActivationEnergyModelingbythetransmutationofdifferent
densityfunctions 67
ShrutiTikhe,KanchanKhare,S.N.Londhe
6 Airqualityforecastingusingsoftcomputingtechniques 105
PalashDutta,BornaliSaikia
7 Arithmeticoperationsongeneralizedsemiellipticintuitionisticfuzzy
numbersandtheirapplicationinmulticriteriadecisionmaking 131
LaxminarayanSahoo
8 Methodforsolvingintuitionisticfuzzyassignmentproblem 155
X Contents
GoutamKumarBose,PritamPain
9 OptimizationofEDMprocessthroughevolutionarycomputingandfuzzy
MCDMtechniques 165
PawanKumar,S.B.Singh
10 Fuzzyreliabilityofsystemusingdifferenttypesofbifuzzyfailureratesof
components 185
Index 215