GMDH-methodology and implementation in C /:
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...
Saved in:
| Other Authors: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Covent Garden, London :
Imperial College Press,
[2015]
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | 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 data mining and knowledge extraction technologies, to several fields including social science, science, engineering, and medicine. This book makes error-free codes available to end-users so that these codes can be used to understand the implementation of GMDH, and then create opportunities to further develop the variants of GMDH algorithms. C-language has been chosen because it is a basic language commonly taught in the first year in computer programming courses in most universities and colleges, and the compiled versions could be used for more meaningful practical applications where security is necessary. |
| Physical Description: | 1 online resource : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781848166110 1848166117 |
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| 245 | 0 | 0 | |a GMDH-methodology and implementation in C / |c editor, Godfrey Onwubolu. |
| 264 | 1 | |a Covent Garden, London : |b Imperial College Press, |c [2015] | |
| 264 | 3 | |a Singapore : |b World Scientific Publishing Co. Pte. Ltd. | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (Ebsco, viewed December 16, 2014). | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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) | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a ""4.1 The COMBI Algorithm""""4.2 Usage of the â€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"" | |
| 505 | 8 | |a 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 | |
| 520 | |a 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 data mining and knowledge extraction technologies, to several fields including social science, science, engineering, and medicine. This book makes error-free codes available to end-users so that these codes can be used to understand the implementation of GMDH, and then create opportunities to further develop the variants of GMDH algorithms. C-language has been chosen because it is a basic language commonly taught in the first year in computer programming courses in most universities and colleges, and the compiled versions could be used for more meaningful practical applications where security is necessary. | ||
| 546 | |a English. | ||
| 650 | 0 | |a GMDH algorithms. |0 http://id.loc.gov/authorities/subjects/sh85055435 | |
| 650 | 0 | |a Self-organizing systems |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85119774 | |
| 650 | 0 | |a C (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh85018532 | |
| 650 | 6 | |a Algorithmes de traitement groupé des données. | |
| 650 | 6 | |a Systèmes auto-organisés |x Informatique. | |
| 650 | 6 | |a C (Langage de programmation) | |
| 650 | 7 | |a SCIENCE |x System Theory. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Operations Research. |2 bisacsh | |
| 650 | 7 | |a C (Computer program language) |2 fast | |
| 650 | 7 | |a GMDH algorithms |2 fast | |
| 650 | 7 | |a Self-organizing systems |x Data processing |2 fast | |
| 700 | 1 | |a Onwubolu, Godfrey C., |e editor. |1 https://id.oclc.org/worldcat/entity/E39PCjybHyymhtqxhCtmHMbMHd |0 http://id.loc.gov/authorities/names/nb2002064428 | |
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| 776 | 0 | 8 | |i Print version: |a Onwubolu, Godfrey. |t GMDH-Methodology and Implementation in C. |d Singapore : Imperial College Press, ©2014 |z 9781848166103 |
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| author_facet | Onwubolu, Godfrey C. |
| building | Verbundindex |
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| callnumber-first | Q - Science |
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| contents | 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 â€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 |
| ctrlnum | (OCoLC)898125741 |
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| dewey-search | 003.7 |
| dewey-sort | 13.7 |
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| discipline | Informatik |
| format | Electronic eBook |
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Ltd.</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2015</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource :</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Online resource; title from PDF title page (Ebsco, viewed December 16, 2014).</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">""4.1 The COMBI Algorithm""""4.2 Usage of the â€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""</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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 data mining and knowledge extraction technologies, to several fields including social science, science, engineering, and medicine. This book makes error-free codes available to end-users so that these codes can be used to understand the implementation of GMDH, and then create opportunities to further develop the variants of GMDH algorithms. 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| id | ZDB-4-EBA-ocn898125741 |
| illustrated | Illustrated |
| indexdate | 2025-03-18T14:22:07Z |
| institution | BVB |
| isbn | 9781848166110 1848166117 |
| language | English |
| lccn | 2015413309 |
| oclc_num | 898125741 |
| open_access_boolean | |
| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
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| physical | 1 online resource : illustrations |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Imperial College Press, |
| record_format | marc |
| spelling | GMDH-methodology and implementation in C / editor, Godfrey Onwubolu. Covent Garden, London : Imperial College Press, [2015] Singapore : World Scientific Publishing Co. Pte. Ltd. ©2015 1 online resource : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (Ebsco, viewed December 16, 2014). Includes bibliographical references and index. 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 â€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 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 data mining and knowledge extraction technologies, to several fields including social science, science, engineering, and medicine. This book makes error-free codes available to end-users so that these codes can be used to understand the implementation of GMDH, and then create opportunities to further develop the variants of GMDH algorithms. C-language has been chosen because it is a basic language commonly taught in the first year in computer programming courses in most universities and colleges, and the compiled versions could be used for more meaningful practical applications where security is necessary. English. GMDH algorithms. http://id.loc.gov/authorities/subjects/sh85055435 Self-organizing systems Data processing. http://id.loc.gov/authorities/subjects/sh85119774 C (Computer program language) http://id.loc.gov/authorities/subjects/sh85018532 Algorithmes de traitement groupé des données. Systèmes auto-organisés Informatique. C (Langage de programmation) SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh C (Computer program language) fast GMDH algorithms fast Self-organizing systems Data processing fast Onwubolu, Godfrey C., editor. https://id.oclc.org/worldcat/entity/E39PCjybHyymhtqxhCtmHMbMHd http://id.loc.gov/authorities/names/nb2002064428 has work: GMDH (Text) https://id.oclc.org/worldcat/entity/E39PCFxwphCXFmfcyQ3yrQGyV3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Onwubolu, Godfrey. GMDH-Methodology and Implementation in C. Singapore : Imperial College Press, ©2014 9781848166103 |
| spellingShingle | GMDH-methodology and implementation in C / 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 â€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 GMDH algorithms. http://id.loc.gov/authorities/subjects/sh85055435 Self-organizing systems Data processing. http://id.loc.gov/authorities/subjects/sh85119774 C (Computer program language) http://id.loc.gov/authorities/subjects/sh85018532 Algorithmes de traitement groupé des données. Systèmes auto-organisés Informatique. C (Langage de programmation) SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh C (Computer program language) fast GMDH algorithms fast Self-organizing systems Data processing fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85055435 http://id.loc.gov/authorities/subjects/sh85119774 http://id.loc.gov/authorities/subjects/sh85018532 |
| title | GMDH-methodology and implementation in C / |
| title_auth | GMDH-methodology and implementation in C / |
| title_exact_search | GMDH-methodology and implementation in C / |
| title_full | GMDH-methodology and implementation in C / editor, Godfrey Onwubolu. |
| title_fullStr | GMDH-methodology and implementation in C / editor, Godfrey Onwubolu. |
| title_full_unstemmed | GMDH-methodology and implementation in C / editor, Godfrey Onwubolu. |
| title_short | GMDH-methodology and implementation in C / |
| title_sort | gmdh methodology and implementation in c |
| topic | GMDH algorithms. http://id.loc.gov/authorities/subjects/sh85055435 Self-organizing systems Data processing. http://id.loc.gov/authorities/subjects/sh85119774 C (Computer program language) http://id.loc.gov/authorities/subjects/sh85018532 Algorithmes de traitement groupé des données. Systèmes auto-organisés Informatique. C (Langage de programmation) SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh C (Computer program language) fast GMDH algorithms fast Self-organizing systems Data processing fast |
| topic_facet | 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 |
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