Fuzzy math ip lpu
Transcript of Fuzzy math ip lpu
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Lovely Professional University, Punjab
Course Code Course Title Course Planner Lectures Tutorials Practicals Credits
MTH402 FUZZY MATHEMATICS 17146::Varun Joshi 3 0 0 3
Course Weightage ATT: 5 CA: 20 MTT: 25 ETT: 50 Exam Category: 13: Mid Term Exam: All MCQ – End Term Exam: MCQ +Subjective
Course Orientation KNOWLEDGE ENHANCEMENT, RESEARCH
TextBooks ( T )
Sr No Title Author Edition Year Publisher Name
T-1 FUZZY SETS AND FUZZY LOGICTHEORY AND APPLICATIONS
GEORGE J. KLIR ANDBO YUAN
2nd 2013 PHI Learning Pvt Ltd
Reference Books ( R )
Sr No Title Author Edition Year Publisher Name
R-1 FUZZY SET THEORY AND ITSAPPLICATIONS
H. J. ZIMMERMANN 4th 2001 SPRINGER
Other Reading ( OR )
Sr No Journals articles as Compulsary reading (specific articles, complete reference)
OR-1 http://fuzzy.cs.uni-magdeburg.de/ci/fs/fs_ch05_relations.pdf ,
OR-2 http://perso.telecom-paristech.fr/~bloch/papers/prDist99.pdf ,
Relevant Websites ( RW )
Sr No (Web address) (only if relevant to the course) Salient Features
RW-1 http://www.fuzzytech.com/ Provides reading materials, softwares, and data analysis relevent to thefuzzy mathematics
RW-2 http://reference.wolfram.com/applications/fuzzylogic/Manual/12.html Fuzzy clustering
RW-3 http://home.deib.polimi. it/matteucc/Clustering/tutorial_html/cmeans.html Fuzzy C-Means Clustering
Detailed Plan For Lectures
LTP week distribution: (LTP Weeks)
Weeks before MTE 7
Weeks After MTE 7
Spill Over (Lecture) 7
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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WeekNumber
LectureNumber
Broad Topic(Sub Topic) Chapters/Sections ofText/referencebooks
Other Readings,Relevant Websites,Audio Visual Aids,software and VirtualLabs
Lecture Description Learning Outcomes Pedagogical ToolDemonstration/Case Study /Images /animation / pptetc. Planned
Live Examples
Week 1 Lecture 1 Fuzzy sets & fuzzy logic-Basic Definitions(Definition
f a fuzzy set)
T-1:1 RW-1 Lecture 1 should beconsidered as zerolecture and in lecture 2introduction to basicdefinitions should be
discussed
Students will be ableto understand aboutthe history of fuzzyset and its evolutionand need of the
subject.They will befamiliar with topicsand evaluationcomponents
Power Pointpresentation withwhite board andmarker.
Application offuzzy set inimagerecognition
Lecture 2 Fuzzy sets & fuzzy logic-Basic Definitions(Definition
f a fuzzy set)
T-1:1 RW-1 Lecture 1 should beconsidered as zerolecture and in lecture 2introduction to basicdefinitions should bediscussed
Students will be ableto understand aboutthe history of fuzzyset and its evolutionand need of thesubject.They will befamiliar with topicsand evaluation
components
Power Pointpresentation withwhite board andmarker.
Application offuzzy set inimagerecognition
Lecture 3 Fuzzy sets & fuzzy logic-Basic Definitions(Elements
f fuzzy logic)
T-1:1 Definition of fuzzy setand its elements, alphacut,strong alpha cut,Level set,Support andheight of fuzzy setConvex fuzzy set
Students will be ableto learn the definitionof fuzzy set and itsparameter like alphacut strong alpha cut
White board andPPT
The database of Cgpa and otherperformance of students andtheirclassification
Week 2 Lecture 4 Fuzzy sets & fuzzy logic-Basic Definitions(Elements
f fuzzy logic)
T-1:1 Definition of fuzzy setand its elements, alphacut,strong alpha cut,Level set,Support andheight of fuzzy set
Convex fuzzy set
Students will be ableto learn the definitionof fuzzy set and itsparameter like alphacut strong alpha cut
White board andPPT
The database of Cgpa and otherperformance of students andtheir
classification
Lecture 5 peration on Fuzzy sets(Unions, Intersections,
omplements,sums,Products differences)
T-1:3 Fuzzy complement,Fuzzy intersections, tnorms, Fuzzy Union ort conorms. Yagerunion,intersection,complement. Boundedand AlgebraicSums,Products,Difference operations on a fuzzyset
Students will learnFuzzy set operationsas generalization of crisp set operations,Students will alsolearn about differentoperations on a Fuzzy
set as a subset of universal set.
White board andPPT along with thediscussion
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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Week 2 Lecture 6 peration on Fuzzy sets(Unions, Intersections,
omplements,sums,Products differences)
T-1:3 Fuzzy complement,Fuzzy intersections, tnorms, Fuzzy Union ort conorms. Yagerunion,intersection,complement. Boundedand AlgebraicSums,Products,Difference operations on a fuzzyset
Students will learnFuzzy set operationsas generalization of crisp set operations,Students will alsolearn about differentoperations on a Fuzzy
set as a subset of universal set.
White board andPPT along with thediscussion
Week 3 Lecture 7 peration on Fuzzy sets
(Unions, Intersections,omplements,
sums,Products differences)
T-1:3 Fuzzy complement,
Fuzzy intersections, tnorms, Fuzzy Union ort conorms. Yagerunion,intersection,complement. Boundedand AlgebraicSums,Products,Difference operations on a fuzzyset
Students will learn
Fuzzy set operationsas generalization of crisp set operations,Students will alsolearn about differentoperations on a Fuzzy
set as a subset of universal set.
White board and
PPT along with thediscussion
Lecture 8 Fuzzy Relations(Join andomposition)
T-1:5R-1:6
Binary fuzzy relation,Join and Compositions(Max-min, Min-Max)
Students will able toconstruct the Joinsand Compositions for
relation matrix
White board andPPT along thediscussion
Fuzzy ternaryrelation by thegraph on CRISP
setLecture 9 Test1
Week 4 Lecture 10 Fuzzy Relations(Relationsincluding, Operations,Reflexivity, Symmetry and
ransitivity)
T-1:5R-1:6
OR-1OR-2
Relations including,Operations, Reflexivityon crisp and Fuzzy setSymmetric Fuzzyrelation on crisp andFuzzy set TransitiveFuzzy relation on crispand Fuzzy set
Students will learnhow thereflexive,symmetricand transitiverelations are used inthe object recognition
Discussion Simple fuzzyimage of alphabet andnumbers onFuzzy imagematrix
Lecture 11 Fuzzy Relations(Relations
including, Operations,Reflexivity, Symmetry andransitivity)
T-1:5
R-1:6
OR-1
OR-2
Relations including,
Operations, Reflexivityon crisp and Fuzzy setSymmetric Fuzzyrelation on crisp andFuzzy set TransitiveFuzzy relation on crispand Fuzzy set
Students will learn
how thereflexive,symmetricand transitiverelations are used inthe object recognition
Discussion Simple fuzzy
image of alphabet andnumbers onFuzzy imagematrix
Lecture 12 Fuzzy Relations(Patternlassification based on
fuzzy relations)
T-1:5 Pattern Classification onfuzzy relationscompatible relation.Fuzzy Orderingrelations
Students will learnhow thereflexive,symmetricand transitiverelations are used inthe object recognition
White board andPPT along thediscussion
Imagerecognition(finger print,face imagematrix)
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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Week 5 Lecture 13 Fuzzy Relations(Patternlassification based on
fuzzy relations)
T-1:5 Pattern Classification onfuzzy relationscompatible relation.Fuzzy Orderingrelations
Students will learnhow thereflexive,symmetricand transitiverelations are used inthe object recognition
White board andPPT along thediscussion
Imagerecognition(finger print,face imagematrix)
Lecture 14 Fuzzy Analysis(Applicationsf Fuzzy sets)
T-1:15 Fuzzy linearprogramming solutionby lower bound andupper bound.Construction
of optimized Fuzzylinear programmingproblem. TriangularFuzzy linearprogramming problem
students will be ableto construct and solvethe problem of Fuzzylinear programming
White board andPPT
Theoptimization ofprofit and loss ofany industrialproblem by
Fuzzy linearprogramming
Lecture 15 Fuzzy Analysis(Applicationsf Fuzzy sets)
T-1:15 Fuzzy linearprogramming solutionby lower bound andupper bound.Constructionof optimized Fuzzylinear programming
problem. TriangularFuzzy linearprogramming problem
students will be ableto construct and solvethe problem of Fuzzylinear programming
White board andPPT
Theoptimization ofprofit and loss ofany industrialproblem byFuzzy linearprogramming
Week 6 Lecture 16 Fuzzy Analysis(Applicationsf Fuzzy sets)
T-1:15 Fuzzy linearprogramming solutionby lower bound andupper bound.Constructionof optimized Fuzzylinear programmingproblem. TriangularFuzzy linear
programming problem
students will be ableto construct and solvethe problem of Fuzzylinear programming
White board andPPT
Theoptimization ofprofit and loss ofany industrialproblem byFuzzy linearprogramming
Lecture 17 Test2
Lecture 18 Fuzzy Analysis(Distancesetween Fuzzy Sets)
T-1:1 Hamming, EuclideanPseudo metric andMalinowski Distancesbetween two fuzzy set
Students will able todetermine thedistance between twofuzzy sets
White board andPPT along thediscussion
The comparisonof image and itsquality fuzzymatrix by thedistances
Week 7 Lecture 19 xtensions, Projections(Cylindrical extensions andlosure , types ofrojections)
T-1:5 Ternary Fuzzy relationand its projections, Cylindrical extensionsand closure
Students will able tofind Closers andExtensions fromternary relation
White board andPPT along with thediscussion
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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SPILL OVERWeek 7 Lecture 20 Spill Over
Lecture 21 Spill Over
MID-TERMWeek 8 Lecture 22 luster Analysis and its
pplication in modellinginformation system(Clustering method, Fuzzy
-mean clusteringmethod,Fuzzy C-meanslgorithm,Clustering methodased upon Fuzzyquivalence relations)
T-1:13 RW-2RW-3
Construction of clusterby Fuzzy compatiblerelation, fuzzyequivalence relation.
Fuzzy Cmean clustering
Students will be ableto differentiate thefuzzy compatible andequivalence relation
and also determinethe C-mean andequivalenceclustering
White board andPPT along thediscussion
Lecture 23 luster Analysis and itspplication in modelling
information system(Clustering method, Fuzzy
-mean clusteringmethod,Fuzzy C-meanslgorithm,Clustering method
ased upon Fuzzyquivalence relations)
T-1:13 RW-2RW-3
Construction of clusterby Fuzzy compatiblerelation, fuzzyequivalence relation.Fuzzy Cmean clustering
Students will be ableto differentiate thefuzzy compatible andequivalence relationand also determinethe C-mean andequivalence
clustering
White board andPPT along thediscussion
Lecture 24 luster Analysis and itspplication in modelling
information system(Clustering method, Fuzzy
-mean clusteringmethod,Fuzzy C-meanslgorithm,Clustering methodased upon Fuzzyquivalence relations)
T-1:13 RW-2RW-3
Construction of clusterby Fuzzy compatiblerelation, fuzzyequivalence relation.Fuzzy Cmean clustering
Students will be ableto differentiate thefuzzy compatible andequivalence relationand also determinethe C-mean andequivalenceclustering
White board andPPT along thediscussion
Week 9 Lecture 25 luster Analysis and its
pplication in modellinginformation system(Clustering method, Fuzzy
-mean clusteringmethod,Fuzzy C-meanslgorithm,Clustering methodased upon Fuzzyquivalence relations)
T-1:13 RW-2
RW-3
Construction of cluster
by Fuzzy compatiblerelation, fuzzyequivalence relation.Fuzzy Cmean clustering
Students will be able
to differentiate thefuzzy compatible andequivalence relationand also determinethe C-mean andequivalenceclustering
White board and
PPT along thediscussion
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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Week 9 Lecture 26 luster Analysis and itspplication in modelling
information system(Clustering method, Fuzzy
-mean clusteringmethod,Fuzzy C-meanslgorithm,Clustering methodased upon Fuzzyquivalence relations)
T-1:13 RW-2RW-3
Construction of clusterby Fuzzy compatiblerelation, fuzzyequivalence relation.Fuzzy Cmean clustering
Students will be ableto differentiate thefuzzy compatible andequivalence relationand also determinethe C-mean andequivalenceclustering
White board andPPT along thediscussion
Lecture 27 Applications of fuzzy sets.(Fuzzy graphs and
onnectivity,Fuzzypplication in Databaseheory, Applications to
Neural Networks)
T-1:12R-1:6
Lecture 27-fuzzy graphand sub graph from
relation.Lecture 28-CompleteFuzzy graph,Pathlength.Lecture 29-Spanninggraph,Connected fuzzygraph, Connectivity.Lecture 30-cycle,Strength MUlength, MU distance.Lecture 31-32:Fuzzydata base.Lecture 33-Applicationto Fuzzy NeuralNetworks
Understanding of readability from one
point to other in afuzzy graphand understanding of fuzzy database andfuzzy informationretrieval with the helpof set of recognizedindex termsand a set of relevantdocuments
White board andPPT along with the
discussion
Week 10 Lecture 28 Applications of fuzzy sets.(Fuzzy graphs andonnectivity,Fuzzypplication in Databaseheory, Applications to
Neural Networks)
T-1:12R-1:6
Lecture 27-fuzzy graphand sub graph fromrelation.Lecture 28-CompleteFuzzy graph,Pathlength.Lecture 29-Spanninggraph,Connected fuzzygraph, Connectivity.Lecture 30-
cycle,Strength MUlength, MU distance.Lecture 31-32:Fuzzydata base.Lecture 33-Applicationto Fuzzy NeuralNetworks
Understanding of readability from onepoint to other in afuzzy graphand understanding of fuzzy database andfuzzy informationretrieval with the helpof set of recognizedindex terms
and a set of relevantdocuments
White board andPPT along with thediscussion
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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Week 10 Lecture 29 Applications of fuzzy sets.(Fuzzy graphs andonnectivity,Fuzzypplication in Databaseheory, Applications to
Neural Networks)
T-1:12R-1:6
Lecture 27-fuzzy graphand sub graph fromrelation.Lecture 28-CompleteFuzzy graph,Pathlength.Lecture 29-Spanninggraph,Connected fuzzygraph, Connectivity.Lecture 30-cycle,Strength MU
length, MU distance.Lecture 31-32:Fuzzydata base.Lecture 33-Applicationto Fuzzy NeuralNetworks
Understanding of readability from onepoint to other in afuzzy graphand understanding of fuzzy database andfuzzy informationretrieval with the helpof set of recognizedindex termsand a set of relevant
documents
White board andPPT along with thediscussion
Lecture 30 Applications of fuzzy sets.(Fuzzy graphs andonnectivity,Fuzzypplication in Databaseheory, Applications to
Neural Networks)
T-1:12R-1:6
Lecture 27-fuzzy graphand sub graph fromrelation.Lecture 28-CompleteFuzzy graph,Pathlength.Lecture 29-Spanninggraph,Connected fuzzygraph, Connectivity.Lecture 30-cycle,Strength MUlength, MU distance.Lecture 31-32:Fuzzydata base.Lecture 33-Applicationto Fuzzy NeuralNetworks
Understanding of readability from onepoint to other in afuzzy graphand understanding of fuzzy database andfuzzy informationretrieval with the helpof set of recognizedindex termsand a set of relevantdocuments
White board andPPT along with thediscussion
Week 11 Lecture 31 Applications of fuzzy sets.(Fuzzy graphs and
onnectivity,Fuzzypplication in Databaseheory, Applications to
Neural Networks)
T-1:12R-1:6
Lecture 27-fuzzy graphand sub graph from
relation.Lecture 28-CompleteFuzzy graph,Pathlength.Lecture 29-Spanninggraph,Connected fuzzygraph, Connectivity.Lecture 30-cycle,Strength MUlength, MU distance.Lecture 31-32:Fuzzydata base.
Lecture 33-Applicationto Fuzzy NeuralNetworks
Understanding of readability from one
point to other in afuzzy graphand understanding of fuzzy database andfuzzy informationretrieval with the helpof set of recognizedindex termsand a set of relevantdocuments
White board andPPT along with the
discussion
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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Week 11 Lecture 32 Applications of fuzzy sets.(Fuzzy graphs andonnectivity,Fuzzypplication in Databaseheory, Applications to
Neural Networks)
T-1:12R-1:6
Lecture 27-fuzzy graphand sub graph fromrelation.Lecture 28-CompleteFuzzy graph,Pathlength.Lecture 29-Spanninggraph,Connected fuzzygraph, Connectivity.Lecture 30-cycle,Strength MU
length, MU distance.Lecture 31-32:Fuzzydata base.Lecture 33-Applicationto Fuzzy NeuralNetworks
Understanding of readability from onepoint to other in afuzzy graphand understanding of fuzzy database andfuzzy informationretrieval with the helpof set of recognizedindex termsand a set of relevant
documents
White board andPPT along with thediscussion
Lecture 33 Applications of fuzzy sets.(Fuzzy graphs andonnectivity,Fuzzypplication in Databaseheory, Applications to
Neural Networks)
T-1:12R-1:6
Lecture 27-fuzzy graphand sub graph fromrelation.Lecture 28-CompleteFuzzy graph,Pathlength.Lecture 29-Spanninggraph,Connected fuzzygraph, Connectivity.Lecture 30-cycle,Strength MUlength, MU distance.Lecture 31-32:Fuzzydata base.Lecture 33-Applicationto Fuzzy NeuralNetworks
Understanding of readability from onepoint to other in afuzzy graphand understanding of fuzzy database andfuzzy informationretrieval with the helpof set of recognizedindex termsand a set of relevantdocuments
White board andPPT along with thediscussion
Week 12 Lecture 34 Test3
Lecture 35 Fuzzy Regression(Fuzzyegression)
T-1:17 Linear regression basicidea with crisp data
Students will be ableto understand thefuzzy regression andconstruct thetransitive relationfrom compatiblematrix for perfectpartitionclassification.
White board andPPT along with thediscussion
The reduction oerror in imageecognition byequivalenceelation
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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Week 12 Lecture 35 Fuzzy Regression(Linearegression basic idea withrisp data)
T-1:17 Linear regression basicidea with crisp data
Students will be ableto understand thefuzzy regression andconstruct thetransitive relationfrom compatiblematrix for perfectpartitionclassification.
White board andPPT along with thediscussion
The reduction oerror in imageecognition byequivalenceelation
Lecture 36 Fuzzy Regression(Linearegression basic idea with
risp data)
T-1:17 Linear regression basicidea with crisp data
Students will be ableto understand the
fuzzy regression andconstruct thetransitive relationfrom compatiblematrix for perfectpartitionclassification.
White board andPPT along with the
discussion
The reduction oerror in image
ecognition byequivalenceelation
Fuzzy Regression(Fuzzyegression)
T-1:17 Linear regression basicidea with crisp data
Students will be ableto understand thefuzzy regression andconstruct thetransitive relation
from compatiblematrix for perfectpartitionclassification.
White board andPPT along with thediscussion
The reduction oerror in imageecognition byequivalenceelation
Week 13 Lecture 37 Fuzzy Regression(LinearRegression with Fuzzy
arameters)
T-1:17 Linear regression withFuzzy parameters
Student will learnlinear regression withFuzzy parameters
White board andPPT along with thediscussion
The reduction oerror in imageecognition byequivalenceelation
Lecture 38 Fuzzy Regression(LinearRegression with Fuzzy
arameters)
T-1:17 Linear regression withFuzzy parameters
Student will learnlinear regression withFuzzy parameters
White board andPPT along with thediscussion
The reduction oerror in imageecognition by
equivalenceelation
Lecture 39 Fuzzy Regression(LinearRegression with Fuzzy Data)
T-1:17 Linear regression withFuzzy data
Student will learnlinear regression withFuzzy data
White board andPPT along with thediscussion
Week 14 Lecture 40 Fuzzy Regression(LinearRegression with Fuzzy Data)
T-1:17 Linear regression withFuzzy data
Student will learnlinear regression withFuzzy data
White board andPPT along with thediscussion
SPILL OVERWeek 14 Lecture 41 Spill Over
Lecture 42 Spill Over
An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.
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Week 15 Lecture 43 Spill Over
Lecture 44 Spill Over
Lecture 45 Spill Over
Scheme for CA:
Component Frequency Out Of Each Marks Total Marks
Test 2 3 30 60
Total :- 30 60
Details of Academic Task(s)
AT No. Objective Topic of the Academic Task Nature of Academic Task(group/individuals/field
work
Evaluation Mode Allottment /submission Week
Test1 To check theunderstanding oftopics fuzzy set,elements of fuzzy
logic, relationsincluding, operationsetc.
Definition of a fuzzy set, Elements of fuzzy logic, Relationsincluding, Operations, Reflexivity, Symmetry and Transitivity.
Individual A test of 30 markswill be conducted.Each question willbe of 5 marks or in
multiple of 5. Allquestions will becompulsary
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Test2 To check thekonwladge ofstudents in Fuzzyanalysis andextention principle
Distances between Fuzzy Sets, Height, Width of Fuzzy Subsets,Continuity and Integrals. Applications of Fuzzy sets extensions,Projections ,Cylindrical extensions and closure , types of projections
Individual A test of 30 markswill be conducted.Each question willbe of 5 marks or inmultiple of 5. Allquestions will becompulsary
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Test3 To test theunderstanding ofApplications offuzzy sets and FuzzyAlgebra.
Paths and Connectedness, Clusters including Cluster Analysis andModelling Information Systems, bApplications, Connectivity inFuzzy Graphs,Application in Database Theory, Applications toNeural Networks
Individual A test of 30 markswill be conducted.Each question willbe of 5 marks or inmultiple of 5. Allquestions will becompulsary
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An instruction plan is a tentative plan only and a teacher may make some changes in his/her teaching plan. The students are advised to use syllabus for preparation of all examinations. The students are expected to keep themselvesupdated on the contemporary issues related to the course. Upto 20% of the questions in any examination/Academic tasks can be asked from such issues even if not explicitly mentioned in the instruction plan.