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The Resource Grey Data Analysis : Methods, Models and Applications

Grey Data Analysis : Methods, Models and Applications

Label
Grey Data Analysis : Methods, Models and Applications
Title
Grey Data Analysis
Title remainder
Methods, Models and Applications
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Contributor
Subject
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Computational Risk Management Ser
Grey Data Analysis : Methods, Models and Applications
Label
Grey Data Analysis : Methods, Models and Applications
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4662694
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Foreword I -- Foreword II -- Foreword III -- Foreword IV -- Preface -- Acknowledgements -- Contents -- About the Authors -- Abstract -- 1 Introduction to Grey Systems Research -- 1.1 Appearance and Growth of Grey Systems Research -- 1.2 Development History and Current State -- 1.3 Characteristics of Uncertain System -- 1.3.1 Incomplete Information -- 1.3.2 Inaccuracies in Data -- 1.3.3 The Scientific Principle of Simplicity -- 1.3.4 Precise Models Suffer from Inaccuracies -- 1.4 Comparison of Several Studies of Uncertain Systems -- 1.5 Most Actively Studied Uncertain Systems Theories -- 1.6 Elementary Concepts of Grey System -- 1.7 Fundamental Principles of Grey Systems -- 2 The Grey Systems Theory Framework -- 2.1 Grey Models and Framework -- 2.2 The Thinking, Models and Framework of Grey Systems Theory -- 2.2.1 Grey Numbers and Its Operations -- 2.2.2 The Grey Sequence Operator -- 2.2.3 The Grey Prediction Models -- 2.2.4 Grey Incidence Analysis Models -- 2.2.5 Grey Clustering Evaluation Models -- 2.2.6 Grey Decision-Making Models -- 2.2.7 Combined Grey Models -- 2.2.8 Grey Control Models -- 2.3 The New Framework and Main Components of Grey Systems Theory -- 3 Grey Numbers and Their Operations -- 3.1 Grey Numbers -- 3.2 The Whitenization of a Grey Number and Degree of Greyness -- 3.3 Degree of Greyness Defined by Axioms -- 3.4 The Operations of Interval Grey Numbers -- 3.5 General Grey Numbers and Their Operations -- 3.5.1 Reduced Form of Interval Grey Numbers -- 3.5.2 General Grey Numbers and Their Reduced Form -- 3.5.3 Synthesis of Degree of Greyness and Operations of General Grey Numbers -- 4 Sequence Operators and Grey Data Mining -- 4.1 Introduction -- 4.2 Systems Under Shocking Disturbances and Buffer Operators -- 4.2.1 The Trap for Shocking Disturbed System Forecasting -- 4.2.2 Axioms that Define Buffer Operators
  • 4.2.3 Properties of Buffer Operators -- 4.3 Construction of Practically Useful Buffer Operators -- 4.3.1 Weakening Buffer Operators -- 4.3.2 Strengthening Buffer Operators -- 4.3.3 The General Form of Buffer Operator -- 4.4 Average Operator -- 4.5 The Quasi-Smooth Sequence and Stepwise Ratio Operator -- 4.6 Accumulating and Inverse Accumulating Operators -- 4.7 Exponentiality of Accumulating Generation -- 5 Grey Incidence Analysis Models -- 5.1 Introduction -- 5.2 Grey Incidence Factors and Set of Grey Incidence Operators -- 5.3 Degrees of Grey Incidences Model -- 5.4 Absolute Degree of Grey Incidence Model -- 5.4.1 Relative and Synthetic Degree of Grey Incidence Models -- 5.4.1.1 Relative Degree of Grey Incidence Model -- 5.4.1.2 Synthetic Degree of Grey Incidence Model -- 5.4.2 Similarity, Closeness and Three-Dimensional Degree of Grey Incidence Models -- 5.4.2.1 Grey Incidence Models Based on Similarity and Closeness -- 5.4.2.2 Three-Dimension Degree of Grey Incidence Models -- 5.5 Superiority Analysis -- 5.5.1 Practical Application -- 6 Grey Clustering Evaluation Models -- 6.1 Introduction -- 6.2 Grey Incidence Clustering Model -- 6.3 Variable Weight Grey Clustering Model -- 6.4 Fixed Weight Grey Clustering Model -- 6.5 Grey Clustering Evaluation Models Based on Mixed Possibility Functions -- 6.5.1 Grey Clustering Evaluation Model Based on End-Point Mixed Possibility Functions -- 6.5.2 Grey Clustering Evaluation Model Based on Center-Point Mixed Possibility Functions -- 6.6 Practical Applications -- 7 Series of GM Models -- 7.1 Introduction -- 7.2 The Four Basic Forms of GM (1, 1) -- 7.2.1 The Basic Forms of Model GM (1, 1) -- 7.2.2 Properties and Characteristics of the Basic Model -- 7.3 Suitable Ranges of Different GM (1, 1) -- 7.3.1 Suitable Sequences of Different GM (1, 1) -- 7.3.2 Applicable Ranges of EGM -- 7.4 Remnant GM (1,1) Model
  • 7.5 Group of GM (1, 1) Models -- 7.6 The Models of GM (r, h) -- 7.6.1 The Model of GM (0, N) -- 7.6.2 The Model of GM (1, N) -- 7.6.3 The Grey Verhulst Model -- 7.6.4 The Models of GM (r, h) -- 7.7 Practical Applications -- 8 Combined Grey Models -- 8.1 Grey Econometrics Models -- 8.1.1 Determination of Variables Using the Principles of Grey Incidence -- 8.1.2 Grey Econometrics Models -- 8.2 Combined Grey Linear Regression Models -- 8.3 Grey Cobb-Douglas Model -- 8.4 Grey Artificial Neural Network Models -- 8.4.1 BP Artificial Neural Model and Computational Schemes -- 8.4.2 Steps in Grey BP Neural Network Modeling -- 8.5 Grey Markov Model -- 8.5.1 Grey Moving Probability Markov Model -- 8.5.2 Grey State Markov Model -- 8.6 Combined Grey-Rough Model -- 8.6.1 Rough Membership, Grey Membership and Grey Numbers -- 8.6.2 Grey Rough Approximation -- 8.6.3 Combined Grey Clustering and Rough Set Model -- 8.7 Practical Applications -- 9 Techniques for Grey Systems Forecasting -- 9.1 Introduction -- 9.2 Interval Forecasting -- 9.3 Grey Disaster Forecasting -- 9.4 Wave Form Forecasting -- 9.5 System Forecasting -- 9.5.1 The Five-Step Modeling Process -- 9.5.2 System Models for Prediction -- 9.6 Practical Applications -- 10 Grey Models for Decision-Making -- 10.1 Introduction -- 10.2 Grey Target Decisions -- 10.3 Other Approaches to Grey Decision -- 10.3.1 Grey Incidence Decision -- 10.3.2 Grey Development Decision -- 10.3.3 Grey Clustering Decision -- 10.4 Multi-attribute Intelligent Grey Target Decision Model -- 10.4.1 The Uniform Effect Measure -- 10.4.2 The Weighted Synthetic Effect Measure -- 10.5 The Paradox of Rule of Maximum Value and Its Solution -- 10.5.1 The Weight Vector Group of Kernel Clustering -- 10.5.2 The Weighted Coefficient Vector of Kernel Clustering for Decision-Making -- 10.5.3 Several Functional Weight Vector Groups of Kernel Clustering
  • 10.6 Practical Applications -- 11 Grey Control Systems -- 11.1 Introduction -- 11.2 Controllability and Observability of Grey System -- 11.3 Transfer Functions of Grey System -- 11.3.1 Grey Transfer Function -- 11.3.2 Transfer Functions of Typical Links -- 11.3.3 Matrices of Grey Transfer Functions -- 11.4 Robust Stability of Grey System -- 11.4.1 Robust Stability of Grey Linear Systems -- 11.4.2 Robust Stability of Grey Linear Time-Delay Systems -- 11.4.3 Robust Stability of Grey Stochastic Linear Time-Delay System -- 11.5 Typical Grey Controls -- 11.5.1 Control with Abandonment -- 11.5.2 Control of Grey Incidence -- 11.5.3 Control of Grey Prediction -- 12 Introduction to Grey Systems Modeling Software -- 12.1 Introduction -- 12.2 Software Features and Functions -- 12.3 Main Components -- 12.4 Operation Guide -- 12.4.1 The Confirmation System -- 12.4.2 Using the Software Package -- Farewell to Our Tutor -- References -- Index
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Extent
1 online resource (351 pages)
Form of item
online
Isbn
9789811018411
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

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