This book reviews the gray system and combines its latest research results in fishery science. The chapters cover the basic concept and theory of gray system, original data processing and gray sequence generation, gray correlation analysis, gray cluster analysis, gray system modeling, gray prediction, gray decision-making, and gray linear programming. The theory of gray system is a new cross-sectional discipline founded in 1982 by Professor Deng Julong, a well-known scholar in China. In recent decades, it has not only been deepened and expanded in theory but also widely used in the fields of society, economy, ocean, agriculture, fishery, and other fields, and made a series of significant scientific achievements. These have laid the foundation for the important position of the gray system theory.
Due to the great uncertainty of the fishery resources and the fishery environment involved in the fishery science system, which is completely different from the natural resources on the land, the data and information belong to the category of “poor information”, and the variability and uncertainty are greater than other natural resources. As an extremely effective analytical method and tool, gray system theory has been applied increasingly in fishery science. The book is developed based on well-read and practical literature and will help scientists and research units engaged in scientific research and teaching in fishery science and related fields to develop new research methods and tools.
Author(s): Xinjun Chen
Publisher: Springer-CAP
Year: 2023
Language: English
Pages: 199
City: Shanghai
Preface
Contents
Chapter 1: Overview of Gray System Theory
1.1 Basic Concepts of the Gray System
1.1.1 Gray Meaning and Gray Phenomenon
1.1.2 Gray System
1.2 Overview of the Development of Gray System Theory
1.2.1 The Scientific Background of Gray System Theory
1.2.2 Overview of the Development of Gray System Theory
1.3 Main Content of the Gray System Theory
1.3.1 Gray Relational Analysis
1.3.2 Gray Modeling
1.3.3 Gray Prediction
1.3.4 Gray Decision-Making
1.3.5 Gray Linear Programming
1.3.6 Gray Control
1.4 The Status and Characteristics of Gray System Theory in Scientific Development
1.4.1 The Position of Gray System Theory in the Discipline System
1.4.2 The Role of Gray System Theory in the Discipline System
1.5 Research Progress on the Application of Gray System Theory in Fishery Science
1.5.1 Fishery Economy Industry
1.5.2 Aquaculture Industry
1.5.3 Environmental Assessment of Fishery Waters
1.5.4 Fisheries Forecasting
References
Chapter 2: Raw Data Processing Method
2.1 Sources and Characteristics of the Original Data
2.2 Several Methods of Raw Data Whitening and Initial Transformation
2.2.1 Collection and Whitening of Raw Data
2.2.2 Several Commonly Used Data Transformation Methods
2.2.2.1 Standardized Transformation
2.2.2.2 Range Transformation
2.2.2.3 Averaging Transformation
2.2.2.4 Initialization Transformation
2.2.2.5 Modular Transformation
2.2.2.6 Moving Average Transformation
2.2.2.7 Transformation of the Weakening Operator and Strengthening Operator
2.2.2.7.1 Weakening Operator Transformation
2.2.2.7.2 Enhanced Operator Transformation
References
Chapter 3: Gray Correlation Analysis
3.1 The Concept of Gray Correlation and Its Application
3.2 Several Calculation Methods of Gray Correlation
3.2.1 General Calculation Method
3.2.2 Absolute Degree of Gray Correlation
3.2.3 Gray Relative Degree of Relevance
3.2.4 Gray Comprehensive Correlation
3.3 Application Examples of Gray Relational Theory in Fishery Science
3.3.1 Application of Gray Correlation in the Analysis of Fishery Industry Structure
3.3.1.1 Correlation Analysis of the Total Aquatic Production with the Production of Seawater and Freshwater
3.3.1.1.1 Correlation Analysis of the Total Aquatic Production and the Production of Seawater and Freshwater Between 1954 and ...
3.3.1.1.2 Correlation Analysis of the Total Aquatic Production and the Production of Seawater and Freshwater in 1978-1984 and ...
3.3.1.2 Correlation Analysis of Seawater and Freshwater Production and Fishing and Aquaculture Production
3.3.1.2.1 Correlation Analysis of Seawater Production and Fishing and Aquaculture Production
3.3.1.2.2 Correlation Analysis of Freshwater Production and Fishing and Aquaculture Production
3.3.1.3 Correlation Analysis of the Production of Seawater and Freshwater and the Production of Each Major Species
3.3.1.3.1 Correlation Analysis of Seawater Production and the Production of Each Major Species
3.3.1.3.2 Correlation Analysis of Freshwater Production and the Production of Major Species
3.3.1.4 Main Conclusions
3.3.1.5 Application of Gray Correlation in the Field of Fishery Resource Assessment
3.3.1.5.1 Selection of Factors Affecting Changes in Fishery Resources
3.3.1.5.2 Selection of Factors Affecting the Catch
3.3.1.5.3 Evaluation of the Abundance of Fishery Resources
3.3.2 Application of Gray Correlation in the Evaluation of Sustainable Use of Fishery Resources
3.3.3 Evaluation of the Influencing Factors of Fishery Water Quality
3.3.4 Application of Gray Correlation in the Aquaculture Industry
3.3.5 Applications in Fishery Biology
3.3.5.1 Analysis of Factors Affecting Fish Behavior
3.3.5.2 Selection and Comparison of Fish Growth Models
3.3.5.3 Gray Correlation Analysis of Fish Morphological Traits and Body Weight
References
Chapter 4: Gray Cluster Analysis
4.1 Concept of Gray Clustering
4.2 Gray Constellation Clustering
4.2.1 Principles and Methods
4.2.2 Example Analysis
4.3 Gray Relational Clustering
4.3.1 The Basic Method of Gray Relational Clustering
4.3.2 Case Analysis
4.4 Gray Variable Weight Clustering
4.4.1 Concept and Method of Gray Variable Weight Clustering
4.4.2 Example Analysis
4.5 Gray Fixed-Weight Clustering
4.5.1 General Method of Gray Fixed-Weight Clustering
4.5.2 Example Analysis
4.6 Analysis of the Application of Gray Clustering in Fishery Science
4.6.1 Application of Gray Clustering in the Fishery Regional Economy
4.6.2 Application of Gray Clustering in the Evaluation of the Nutritional Value of Fish
4.6.2.1 Determination of Clustering Factors
4.6.2.2 Dimensionless Calculation
4.6.2.3 Determine the Whitening Functions of Various Types
4.6.2.4 Find the Clustering Weight of Each Item
4.6.2.5 Calculate the Clustering Coefficient
4.6.2.6 Construct a Clustering Vector and Classify It According to the Maximum Principle
4.6.3 Application of Gray Clustering Analysis in the Environmental Assessment of Fishery Waters
4.6.3.1 Dimensionless Processing
4.6.3.2 Calculating the Clustering Weight
4.6.3.3 Calculation of the Clustering Coefficient
4.6.3.4 Evaluation Results
4.6.4 Application of Gray Clustering in the Classification of Fish Populations
References
Chapter 5: Basic Principles of Gray Dynamic Modeling
5.1 Technical Route of Gray Modeling
5.1.1 Principle of Gray Dynamic Modeling
5.1.2 Common GM (n, h) Model
5.1.2.1 GM (n, 1) Model
5.1.2.2 GM (1, h) Model
5.2 GM (1, 1) Model
5.2.1 The Basic Steps of Establishing the GM (1, 1) Model
5.2.2 Different Forms of the GM (1, 1) Model
5.2.3 Interpretation and Analysis of the Development Coefficient-a Value
5.3 GM (1, n) Model of Gray Sequence
References
Chapter 6: Gray Prediction
6.1 Test Method of the Gray Prediction Model
6.1.1 Absolute Correlation Test Method
6.1.2 Mean Square Error Ratio and Small Error Probability Test Method
6.2 Sequence Prediction
6.3 Prediction of Gray Catastrophe
6.3.1 Gray Catastrophe Prediction
6.3.2 Prediction of Gray Seasonal Catastrophe
6.4 Application of Gray Prediction in Fishery Science
6.4.1 Gray Prediction of Fishery Yield
6.4.1.1 Gray Prediction of Marine Catch in the Indian Ocean
6.4.2 Gray Prediction of Shrimp Production in the Bohai Sea
6.4.3 Gray Prediction of Fishery Human Resources
6.4.3.1 Gray Prediction of Fishery Labor
6.4.3.2 Gray Prediction of Fishing Labor
6.4.3.3 Gray Prediction of the Farming Labor
6.4.3.4 Gray Prediction of Service Labor
6.4.3.5 Gray Prediction of Part-Time Labor
6.4.4 Application of the Gray System in Fishery Forecasting
6.4.4.1 Gray Prediction of the Peak Fishing Season of Ommastrephes bartramii
6.4.4.1.1 Data and Study Methods
6.4.4.1.2 Research Results
Analysis of Fishing Season Characteristics
Analysis of Peak Fishing Season
6.4.4.2 Establishment of a Gray Prediction Model for the Abundance Index of Ommastrephes bartramii
6.4.4.2.1 Research Data and Methods
6.4.4.2.2 Selection of Model Time Series
6.4.4.2.3 Selection of Influencing Factors
6.4.4.2.4 Construction and Comparison of Gray Models
6.4.4.3 Resource Forecasting of the Pacific Stock of Scomber australasicus Based on the Gray System
6.4.4.3.1 Materials and Methods
6.4.4.3.2 Correlation Analysis of Monthly Temperature Factors and Resources of Australian Mackerel
6.4.4.3.3 Analysis of Environmental Factors Affecting the Resources of Australian Mackerel Based on Gray Correlation
6.4.4.3.4 Establishment of a Gray Forecast Model for Australian Mackerel Resources
6.4.4.3.5 Comparison and Validation of Gray Prediction Models
6.4.4.4 Gray Prediction of Diseases of Large Yellow Croaker in Cage Culture
6.4.4.4.1 Correlation Analysis of the Morbidity of Large Yellow Croaker and Environmental Factors
6.4.4.4.2 Establishment of the GM (1, N) Model
6.4.4.5 Prediction of Gray Catastrophe of Fishery Resources
6.4.4.5.1 Prediction of Gray Catastrophe for the Resource Abundance of Ommastrephes bartramii
Data and Methods
CPUE Distribution and Its Catastrophe Point
Establishment and Validation of the Gray Catastrophe Model
6.4.4.5.2 Gray Catastrophe Prediction of the Abundance of Illex argentinus in the Waters of the Malvinas Islands
Data and Research Methods
Changes in the Production and CPUE of Illex argentinus
Establishment and Testing of the GM (1, 1) Model
References
Chapter 7: Gray Decision
7.1 Basic Concepts of Gray Decision-Making
7.2 Gray Correlation Decision-Making
7.2.1 Basic Concepts
7.2.2 Basic Steps of Gray Correlation Decision-Making Calculation
7.3 Decision-Making in the Gray Situation
7.3.1 Decision Element, Decision Vector, and Decision Matrix
7.3.2 Effectiveness Measure
7.3.3 Multiobjective Decision Matrix
7.3.4 Decision-Making Criteria
7.3.5 Prioritized Decision Matrix
7.3.6 Normalized Decision Matrix
7.4 Analysis of the Application of the Gray Decision-Making System in Fishery Science
7.4.1 Application in the Fish Farming Industry
7.4.2 Application of Environmental Assessment of Fishery Waters
7.4.2.1 Determine the Event Set, Countermeasure Set, and Target Set
7.4.2.2 Calculate the Measure of Target Effectiveness
7.4.2.3 Determination of Target Weights
7.4.2.4 Calculate the Comprehensive Effect Measure
7.4.2.5 Determining the Optimal Situation
References
Chapter 8: Gray Linear Programming
8.1 Gray Linear Programming Model
8.1.1 Standard Form of the Linear Programming Model
8.1.2 Gray Linear Programming
8.2 Application of Gray Linear Programming in Fishery Science
8.2.1 Analysis of the Structure of Different Fishing Vessels
8.2.2 The Planning of the Number of Fishing Vessels and Their Efforts in Marine Fishing Operations in Shandong Province
8.2.3 Linear Programming Model for the Marine Aquaculture Industry
References
