GMDH-methodology and implementation in C /

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Group Method of Data Handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modeling has been developed and applied to complex systems in areas like prediction, modeling, clusterization, system identification, as well as...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Onwubolu, Godfrey C. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Covent Garden, London : Imperial College Press, [2015]
Temas:
GMDH algorithms.
Self-organizing systems > Data processing.
C (Computer program language)
Algorithmes de traitement groupé des données.
Systèmes auto-organisés > Informatique.
C (Langage de programmation)
SCIENCE > System Theory.
TECHNOLOGY & ENGINEERING > Operations Research.
GMDH algorithms
Self-organizing systems > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Contents
  • Preface
  • Organization of the Chapters
  • Intended Audience
  • Resources for Readers
  • About the Editor
  • List of Contributors
  • 1. Introduction
  • 1.1 Historical Background of GMDH
  • 1.2 Basic GMDH Algorithm
  • 1.2.1 External criteria
  • 1.3 GMDH-Type Neural Networks
  • 1.4 Classification of GMDH Algorithms
  • 1.4.1 Parametric GMDH algorithms
  • 1.4.1.1 Multilayer GMDH
  • 1.4.1.2 Combinatorial GMDH
  • 1.4.1.3 Objective system analysis
  • 1.4.2 Non-parametric GMDH algorithms
  • 1.4.2.1 Objective cluster analysis (OCA)
  • 1.4.2.2 Analogue complexing (AC)1.4.2.3 Pointing finger clusterization algorithm
  • 1.5 Rationale for GMDH in C Language
  • 1.6 Available Public Software
  • 1.7 Recent Developments
  • 1.8 Conclusions
  • References
  • 2. GMDH Multilayered Iterative Algorithm (MIA)
  • 2.1 Multilayered Iterative Algorithm (MIA) Networks
  • 2.1.1 GMDH layers
  • 2.1.2 GMDH nodes
  • 2.1.3 GMDH connections
  • 2.1.4 GMDH network
  • 2.1.5 Regularized model selection
  • 2.1.6 GMDH algorithm
  • 2.2 Computer Code for GMDH-MIA
  • 2.2.1 Compute a tree of quadratic polynomials
  • 2.2.2 Evaluate the Ivakhnenko polynomial using the tree of polynomials generated2.2.3 Compute the coefficients in the Ivakhnenko polynomial using the same tree of polynomials generated
  • 2.2.4 Main program
  • 2.3 Examples
  • 2.3.1 Example 1
  • 2.3.2 Example 2
  • 2.4 Summary
  • References
  • 3. GMDH Multilayered Algorithm Using Prior Information
  • 3.1 Introduction
  • 3.2 Criterion Correction Algorithm
  • 3.3 C++ Implementation
  • 3.3.1 Building sources
  • 3.4 Example
  • 3.5 Conclusion
  • References
  • 4. Combinatorial (COMBI) Algorithm
  • ""4.1 The COMBI Algorithm""""4.2 Usage of the “Structure of Functionsâ€?""; ""4.3 Gradual Increase of Complexity""; ""4.4 Implementation""; ""4.5 Output Post-Processing""; ""4.6 Output Interpretation""; ""4.7 Predictive Model""; ""4.8 Summary""; ""References""; ""5. GMDH Harmonic Algorithm""; ""5.1 Introduction""; ""5.2 Polynomial Harmonic Approximation""; ""5.2.1 Polynomial, harmonic and hybrid terms""; ""5.2.2 Hybrid function approximation""; ""5.2.3 Need for hybrid modelling""; ""5.3 GMDH Harmonic""; ""5.3.1 Calculation of the non-multiple frequencies""
  • 5.3.2 Isolation of significant harmonics5.3.3 Computing of the harmonics
  • Appendix A. Derivation of the trigonometric equations
  • A.1 System of equations for the weighting coefficients
  • A.2 Algebraic equation for the frequencies
  • A.3 The normal trigonometric equation
  • References
  • 6. GMDH-Based Modified Polynomial Neural Network Algorithm
  • 6.1 Modified Polynomial Neural Network
  • 6.2 Description of the Program of MPNN Calculation
  • 6.2.1 The software framework (GMDH)
  • 6.2.2 Object-oriented architecture of the software framework

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