Conventional and fuzzy regression : theory and engineering applications /

"Aims to present both conventional and fuzzy regression analyses from theoretical aspects followed by application examples. The present book contains chapters originating from different scientific fields. The first deals with both crisp (conventional) linear or nonlinear regression and fuzzy li...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Hrissanthou, Vlassios (Editor ), Spiliotis, Mike (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Nova Science Publishers, Inc., [2018]
Colección:Environmental science, engineering and technology
Temas:
Engineering mathematics.
Fuzzy statistics.
Regression analysis.
Mathématiques de l'ingénieur.
Statistique floue.
Analyse de régression.
MATHEMATICS / Applied.
MATHEMATICS / Probability & Statistics / General.
Engineering mathematics
Fuzzy statistics
Regression analysis
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 3.2. Predictive Analytics in Internet of Things (IoT) Based Systems
  • 3.3. Coding Theory: Extrinsic Information Scaling in Turbo Codes
  • Conclusion
  • Acknowledgments
  • References
  • About the Authors
  • Chapter 4
  • From Global to Local: GWR as an Exploratory Tool for Spatial Phenomena
  • Abstract
  • Introduction
  • Issues Emerging in Spatial Phenomena Research
  • Spatial Dependence and Spatial Autocorrelation
  • Spatial Heterogeneity/Spatial Non-Stationarity
  • Spatial Expansion Method and Local Weighted Regression
  • Geographically Weighted Regression (GWR)
  • GWR Equation, Kernel and Bandwidth Choice
  • Statistical Significance Levels and Statistical Significance of Coefficient Non-Stationarity
  • Multicollinearity in GWR
  • GWR Extensions
  • Example
  • Data
  • Methodology
  • Results
  • Conclusion
  • References
  • Biographical Sketches
  • Chapter 5
  • Fuzzy Regression Using Triangular Fuzzy Number Coefficients: Similarities of the Calibrated Fuzzy Models
  • Abstract
  • Introduction
  • Symmetric Triangular Fuzzy Numbers
  • Principles of Fuzzy Linear Regression
  • An Application of Fuzzy Linear Regression Based on Symmetric Triangular Fuzzy Numbers
  • Forecast with the Method of Fuzzy Linear Regression
  • Comparison of the Forecasting Accuracy and Ability of the Fuzzy and the Classical Linear Regression
  • Similarities in Fuzzy Regression Models
  • Fuzzy Classification Using Similarity Ratios
  • An Application of Similarity Ratios and Fuzzy Classification
  • Discussion
  • Conclusion
  • References
  • Biographical Sketches
  • Chapter 6
  • Models of Fuzzy Linear Regression with Trapezoidal Membership Functions: Application in Hydrology
  • Abstract
  • 1. Introduction
  • 2. Mathematical Model
  • 2.1. Bisserier Model (2010)
  • 2.1.1. Generalities
  • 2.1.2. Identification Procedure
  • 2.1.2.1. Optimization Criterion
  • 2.1.2.2. Constraints. 2.1.3. Tendency Problem
  • 2.2. Fung et al. (2006) Model
  • 2.2.1. Generalities
  • 2.2.2. Identification Procedure
  • 2.2.2.1. Optimization Criterion
  • 2.2.2.2. Constraints
  • 2.2.3. Modified Model
  • 2.3. Model of Tzimopoulos et al. (2016)
  • 2.3.1. Generalities
  • 2.3.2. Step 1
  • 2.3.2. Step 2
  • 3. Applications
  • 3.1. Application 1
  • 3.1.1. Bisserier Shift Model
  • 3.1.2. Fung et al. Model (initial)
  • 3.1.3. Fung et al. Model (modified)
  • 3.1.4. Tzimopoulos et al. Model
  • 3.2. Application 2: A Hydrological Problem in the Region of Western Macedonia (Northern Greece)
  • 3.2.1. Step 1
  • 3.2.2. Step 2
  • Conclusion
  • References
  • Biographical Sketches
  • Chapter 7
  • Strength Determination of Fiber-Reinforced Soils Based on Multivariable Ordinary and Fuzzy Linear Regression Analyses
  • Abstract
  • Introduction
  • Experimental Measurements
  • Methods of analysis
  • Multivariable Ordinary (Conventional) Linear Regression Method
  • Fuzzy Linear Regression Method
  • Determination of Model Credibility
  • Development of Models
  • Efficiency and Comparison of Models
  • Conclusion
  • Acknowledgments
  • References
  • Biographical Sketches
  • Chapter 8
  • Eutrophication in a Mediterranean Lake using Fuzzy Linear Regression Method with Fuzzy Data
  • Abstract
  • 1. Introduction
  • 2. Methodology
  • 2.1. Study Area and Data Base
  • 2.2. Description of the Fuzzy Model
  • 2.2.1. Min Problem
  • 2.2.2. Max Problem
  • 2.2.3. The Least Squares Model
  • 3. Results-Discussion
  • Conclusion
  • Appendix I
  • An Application in Engineering Using the Methods of Min, Max and Least Squares
  • Appendix II
  • References
  • Biographical Sketches
  • About the Editors
  • Index
  • Blank Page.

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