Perceptual Computing
Aiding People in Making Subjective Judgments
Published on 3. August 2010
256 pages
978-0-470-59963-1 (ISBN)
Description
Explains for the first time how "computing with words" can aidin making subjective judgments
Lotfi Zadeh, the father of fuzzy logic, coined the phrase"computing with words" (CWW) to describe a methodology in which theobjects of computation are words and propositions drawn from anatural language. Perceptual Computing explains how to implementCWW to aid in the important area of making subjective judgments,using a methodology that leads to an interactive device--a"Perceptual Computer"--that propagates random and linguisticuncertainties into the subjective judgment in a way that can bemodeled and observed by the judgment maker.
This book focuses on the three components of a PerceptualComputer--encoder, CWW engines, and decoder--and thenprovides detailed applications for each. It uses interval type-2fuzzy sets (IT2 FSs) and fuzzy logic as the mathematical vehiclefor perceptual computing, because such fuzzy sets can modelfirst-order linguistic uncertainties whereas the usual kind offuzzy sets cannot. Drawing upon the work on subjective judgmentsthat Jerry Mendel and his students completed over the past decade,Perceptual Computing shows readers how to:
* Map word-data with its inherent uncertainties into an IT2 FSthat captures these uncertainties
* Use uncertainty measures to quantify linguisticuncertainties
* Compare IT2 FSs by using similarity and rank
* Compute the subsethood of one IT2 FS in another such set
* Aggregate disparate data, ranging from numbers to uniformlyweighted intervals to nonuniformly weighted intervals to words
* Aggregate multiple-fired IF-THEN rules so that the integrity ofword IT2 FS models is preserved
Free MATLAB-based software is also available online so readerscan apply the methodology of perceptual computing immediately, andeven try to improve upon it. Perceptual Computing is animportant go-to for researchers and students in the fields ofartificial intelligence and fuzzy logic, as well as for operationsresearchers, decision makers, psychologists, computer scientists,and computational intelligence experts.
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Jerry Mendel | Dongrui Wu
Perceptual Computing - Aiding People in Making ective Judgments
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08/2010
Wiley
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Wiley-IEEE Press
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Persons
Jerry M. Mendel is Professor of Electrical Engineering atthe University of Southern California. A Life Fellow of the IEEEand a Distinguished Member of the IEEE Control Systems Society,Mendel is also the recipient of many awards for his diverseresearch, including the IEEE Centennial Medal, the Fuzzy SystemsPioneer Award from the IEEE Computational Intelligence Society, andthe IEEE Third Millennium Medal.
Dongrui Wu is a Postdoctoral Research Associate at theUniversity of Southern California, where he recently obtained hisPhD in electrical engineering.
Content
Preface.
1 Introduction.
1.1 Perceptual Computing.
1.2 Examples.
1.3 Historical Origins of Perceptual Computing.
1.4 How to Validate the Perceptual Computer.
1.5 The Choice of Fuzzy Set Models for the Per-C.
1.6 Keeping the Per-C as Simple as Possible.
1.7 Coverage of the Book.
1.8 High-Level Synopses of Technical Details.
References.
2 Interval Type-2 Fuzzy Sets.
2.1 A Brief Review of Type-1 Fuzzy Sets.
2.2 Introduction to Interval Type-2 Fuzzy Sets.
2.3 Definitions.
2.4 Wavy-Slice Representation Theorem.
2.5 Set-Theoretic Operations.
2.6 Centroid of an IT2 FS.
2.7 KM Algorithms.
2.8 Cardinality and Average Cardinality of an IT2 FS.
2.9 Final Remark.
Appendix 2A. Derivation of the Union of Two IT2 FSs.
Appendix 2B. Enhanced KM (EKM) Algorithms.
References.
3 Encoding: From a Word to a Model--TheCodebook.
3.1 Introduction.
3.2 Person FOU Approach for a Group of Subjects.
3.3 Collecting Interval End-Point Data.
3.4 Interval End-Points Approach.
3.5 Interval Approach.
3.6 Hedges.
Appendix 3A. Methods for Eliciting T1 MF Information FromSubjects.
Appendix 3B. Derivation of Reasonable Interval Test.
References.
4 Decoding: From FOUs to a Recommendation.
4.1 Introduction.
4.2 Similarity Measure Used as a Decoder.
4.3 Ranking Method Used as a Decoder.
4.4 Classifier Used as a Decoder.
5 Novel Weighted Averages as a CWW Engine.
5.1 Introduction.
5.2 Novel Weighted Averages.
5.3 Interval Weighted Average.
5.4 Fuzzy Weighted Average.
5.5 Linguistic Weighted Average.
5.6 A Special Case of the LWA.
5.7 Fuzzy Extensions of Ordered Weighted Averages.
6 IF-THEN Rules as a CWW Engine--PerceptualReasoning.
6.1 Introduction.
6.2 A Brief Overview of Interval Type-2 Fuzzy Logic Systems.
6.3 Perceptual Reasoning: Computations.
6.4 Perceptual Reasoning: Properties.
7 Assisting in Making Investment Choices--InvestmentJudgment Advisor (IJA).
7.1 Introduction.
7.2 Encoder for the IJA.
7.3 Reduction of the Codebooks to User-Friendly Codebooks.
7.4 CWW Engine for the IJA.
7.5 Decoder for the IJA.
7.6 Examples.
7.7 Interactive Software for the IJA.
7.8 Conclusions.
References.
8 Assisting in Making Social Judgments--Social JudgmentAdvisor (SJA).
8.1 Introduction.
8.2 Design an SJA.
8.3 Using an SJA.
8.4 Discussion.
8.5 Conclusions.
References.
9 Assisting in Hierarchical Decision Making--ProcurementJudgment Advisor (PJA).
9.1 Introduction.
9.2 Missile Evaluation Problem Statement.
9.3 Per-C for Missile Evaluation: Design.
9.4 Per-C for Missile Evaluation: Examples.
9.5 Comparison with Previous Approaches.
9.6 Conclusions.
Appendix 9A: Some Hierarchical Multicriteria Decision-MakingApplications.
References.
10 Assisting in Hierarchical and Distributed DecisionMaking-- Journal Publication Judgment Advisor (JPJA)/
10.1 Introduction.
10.2 The Journal Publication Judgment Advisor (JPJA).
10.3 Per-C for the JPJA.
10.4 Examples.
10.4.5 Complete Reviews.
10.5 Conclusions.
Reference.
11 Conclusions.
11.1 Perceptual Computing Methodology.
11.2 Proposed Guidelines for Calling Something CWW.
Index.
