This book aims to provide a practical guide by a set of real world applications of grey systems to social networks, energy management, transportation, natural disaster management, etc. As an emerging subject on data analysis and uncertainty modelling, the theory of grey systems and its applications have seen significant progress in recent years. However, researchers are still challenged with difficulties in differentiating grey systems from other similar models and a concise and practical guide for their real world applications. This is especially true for researchers in Europe and North American. This book may provide the answer for that. This book is a result of work within the Leverhulme Trust International Research Network on Grey Systems and its Applications. Readers may regard the book as valuable reference in the related fields.
Author(s): Yingjie Yang, Sifeng Liu
Series: Series on Grey System
Publisher: Springer
Year: 2023
Language: English
Pages: 334
City: Singapore
Series Preface
Preface
Contents
1 Development of Grey System Research
1.1 The Development of Grey Systems
1.2 The Father of Grey System Theory
1.3 Institute for Grey System Studies, NUAA
1.4 A Summary on the MSCA Project GS-A-DM-DS (629051)
1.5 Grey Systems—Where We Are Now
References
2 Basic Models in Grey System Theory
2.1 Introduction
2.2 General Framework of Grey System Models
2.2.1 Framework of Grey System Models
2.2.2 Mechanism of Limited Data Grey Modeling
2.2.3 Mechanism of Grey Number Modeling
2.3 Introduction to Grey Forecasting Model
2.3.1 General Evolution of Grey Forecasting Models
2.3.2 Steps of GM(1, 1) Model
2.3.3 Steps of DGM(1, N) Model
2.3.4 Future Thinking About Grey Forecasting Models
2.4 Introduction to Grey Relational Model
2.4.1 General Evolution of Grey Relational Models
2.4.2 Steps of Deng’s Grey Relational Model
2.4.3 Steps of Generalized Grey Relational Model
2.4.4 Future Thinking About Grey Relational Models
2.5 Introduction to Grey Cluster Model
2.5.1 General Evolution of Grey Cluster Models
2.5.2 Steps of Variable Weight Grey Cluster Model
2.5.3 Future Thinking About Grey Cluster Models
2.6 Introduction to Grey Target Model
2.6.1 General Evolution of Grey Target Models
2.6.2 Steps of Grey Target Model
2.7 Conclusions
References
3 Grey Systems and Uncertainty Modelling
3.1 Introduction
3.2 Uncertainties and Uncertainty Models
3.3 Fuzzy Sets and Rough Sets
3.4 Greyness and Grey Sets
3.5 Relationships Between Different Models
3.6 Roles of Grey Systems in Uncertainty Modelling
3.6.1 A Media for Uncertainty Modelling
3.6.2 Uncertainty Modelling in Data Analytics
3.7 Conclusions
References
4 Extending Neuro-fuzzy Techniques with Grey-Based Hybridisation
4.1 Introduction
4.2 Fuzzy Cognitive Maps
4.2.1 Theoretical Background
4.2.2 FCM Dynamic Analysis
4.2.3 FCM Consensus
4.3 Fuzzy Grey Cognitive Maps
4.3.1 Theoretical Background
4.3.2 FGCM Dynamic Analysis
4.3.3 FGCM Consensus
4.4 Conclusions
References
5 The Grey Structure and Evolution of Knowledge Towards a Theory of Grey Knowledge Conception
5.1 What Is the Grey Knowledge?
5.2 The Representation and Analysis of the Grey Knowledge
5.3 Conclusions and Future Works
References
6 Agent-Based Modelling in Grey Economic Systems
6.1 Grey Economic Systems
6.1.1 Simple Versus Complex Systems
6.1.2 Economic Systems
6.1.3 Grey Economic Systems
6.2 Grey Numbers and Their Operations
6.3 Research on Agent-Based Modelling
6.3.1 Economic Modelling
6.3.2 Agent-Based Modelling
6.3.3 Some Examples
6.4 Influence in Online Social Media Environments
6.4.1 Prerequisites
6.4.2 NetLogo Implementation
6.5 Concluding Remarks
ANNEX—The NetLogo Program for Consumer Opinion
References
7 Forecasting and Optimization of Short-Term Traffic Flow Dynamics Modeling Based on Grey System Theory
7.1 Introduction
7.2 The GM(1, 1|τ, r) Model for Forecasting the Urban Road Short-Term Traffic Flow
7.2.1 The GM(1, 1|τ, r) Model and Its Properties
7.2.2 The Determination for Traffic System Delay Time
7.2.3 The Determination for Nonlinear Factor
7.2.4 Example Analyses
7.2.5 Conclusions
7.3 Grey Coupled Prediction Model of Traffic Flow with Panel Data Characteristics
7.3.1 DGM(1, 1) Model
7.3.2 Research on the CTAGO Operator
7.3.3 DGM(1, 1) Model of the CTAGO Operation
7.3.4 Rolling Discrete Grey Model(RDGM(1, 1))
7.3.5 Rolling Seasonal Discrete Grey Model(RSDGM(1, 1))
7.3.6 RSDGM(1, 1)-ARIMA Model
7.3.7 Numerical Examples
7.3.8 Conclusions
7.4 ITS Dynamic Optimization Design Based on Grey System Theory
7.4.1 ITS Optimization Design Based on Grey System System
7.4.2 The Design of an Intelligent Traffic Signal Optimization Control System
7.4.3 Empirical Analysis
7.4.4 Conclusions
References
8 Yellow River Ice Disaster Risk Management Based on Grey Prediction and Decision Method
8.1 Research Profile of Yellow River Ice Disaster Risk Management
8.1.1 Profile of Yellow River Ice Flood and Its Disaster
8.1.2 Profile of Yellow River Ice Disaster Risk Management
8.2 Risk Factors of Yellow River Ice Disaster
8.2.1 Analysis of Main Features and Influence Factors of Yellow River Ice Disaster
8.2.2 The Main Influencing Factor of Yellow River Ice Disaster Risk
8.3 Risk Assessment of Ice Disaster in the Yellow River
8.3.1 Risk Assessment of Ice Disaster Based on Hybrid Grey Multiple-Attribute Decision-Making Method
8.3.2 Risk Assessment of Ice Disaster Based on Grey Phase Relation Decision-Making Method
8.3.3 Risk Assessment of Ice Disaster Based on Grey Rough Combined Decision-Making Method
8.4 Risk Prediction of Yellow River Ice Disaster
8.4.1 Risk Prediction of Yellow River Ice Disaster Based on Model GM(1, 1)
8.4.2 Risk Prediction of Yellow River Ice Disaster Based on Model GMP(1, 1, N)
8.5 Summary
References
9 Millennial Generation: Enthusiastic Versus Stressed Consumers. How Different Is Their Behaviour and Their Opinion on Companies’ Image in Online Social Networks?
9.1 Introduction
9.2 Researches on Online Social Networks
9.3 Research Methodology
9.4 Confirmatory Factor Analysis with AMOS 22.0.0
9.5 The Degrees of Grey Incidence
9.6 Case Study: Enthusiastic Versus Stressed Consumers
9.7 Questionnaire and Data Analysis
9.8 Grey Incidence Analysis
9.9 Millennials Characteristics—A Summary
9.10 Conclusions
References
10 Grey Models and Its Application in Energy-Economy System
10.1 Introduction
10.1.1 The Greyness of Energy-Economy System Analysis
10.1.2 Literature Review
10.2 Energy Consumption Predictions with Grey Models
10.2.1 China’s Primary Energy Consumption Predictions
10.2.2 Global Primary Energy Consumption Predictions
10.3 Grey Incidence Analysis of Energy-Economy System
10.3.1 The Relationship Between China’s Energy Consumption and Economic Growth
10.3.2 The Relation Between China’s Gasoline Prices and Crude Oil Price
10.4 Grey Programming Models for a Multi-factors Energy-Environment-Economy System
10.4.1 The Grey Programming Model
10.4.2 China’s Relation of Economic Development and CO2 Emission
10.4.3 Results and Discussions
References
11 Selection of the Company’s Strategy Using Grey Stratified Decisions Model
11.1 Introduction
11.2 Current State of Research in the Field GST Applications in Strategic Management
11.3 Analysis of the Potential and Environment in Strategic Management
11.4 Grey Stratified Decisions Model as a Tool for Choosing Corporate Strategies
11.5 Case Study
11.6 Conclusion
References
12 A Cost Level Analysis for the Components of the Smartphones Using Greyness Based Quality Function Deployment
12.1 Introduction: Background and Driving Forces
12.2 A Brief Technical Analysis of Smartphones
12.3 Customer Requirements on Smartphones
12.4 Material and Methods
12.4.1 The Basic Concept of Quality Function Deployment (QFD)
12.4.2 Grey System Theory: Degree of Greyness
12.5 Case Study
12.6 Conclusion
References
