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...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Nova Science Publishers, Inc.,
[2018]
|
| Schriftenreihe: | Environmental science, engineering and technology
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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 linear or nonlinear regression. The application example refers to the relationship between sediment transport rates on the one hand and stream discharge and rainfall intensity on the other hand. Second chapter refers to the crisp linear or nonlinear regression of six heavy metals between different soft tissues and shells of Telescopium telescopium and its habitat surface sediments. Third describes the crisp linear, multiple linear, nonlinear and Gaussian process regressions. The fourth is confronted with a classic regression model, named Geographically Weighted Regression (GWR), which constitutes a spatial statistics method. The fifth chapter regards fuzzy linear regression based on symmetric triangular fuzzy numbers. The sixth chapter treats fuzzy linear regression based on trapezoidal membership functions. The main application of this chapter concerns the dependence of rainfall records between neighboring rainfall stations for a small sample of data. The next chapter refers to the multivariable crisp and fuzzy linear regression. The eighth chapter deals with the fuzzy linear regression, with crisp input data and fuzzy output data. All the chapters offer a proper foundation of either widely used or new techniques upon regression. Among the new techniques, several innovated fuzzy regression based methodologies are developed for real problems, and useful conclusions are drawn"-- |
| Beschreibung: | 1 online resource. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781536137996 1536137995 |
Internformat
MARC
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| 245 | 0 | 0 | |a Conventional and fuzzy regression : |b theory and engineering applications / |c Vlassios Hrissanthou and Mike Spiliotis, editors. |
| 264 | 1 | |a New York : |b Nova Science Publishers, Inc., |c [2018] | |
| 300 | |a 1 online resource. | ||
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| 490 | 0 | |a Environmental science, engineering and technology | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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 linear or nonlinear regression. The application example refers to the relationship between sediment transport rates on the one hand and stream discharge and rainfall intensity on the other hand. Second chapter refers to the crisp linear or nonlinear regression of six heavy metals between different soft tissues and shells of Telescopium telescopium and its habitat surface sediments. Third describes the crisp linear, multiple linear, nonlinear and Gaussian process regressions. The fourth is confronted with a classic regression model, named Geographically Weighted Regression (GWR), which constitutes a spatial statistics method. The fifth chapter regards fuzzy linear regression based on symmetric triangular fuzzy numbers. The sixth chapter treats fuzzy linear regression based on trapezoidal membership functions. The main application of this chapter concerns the dependence of rainfall records between neighboring rainfall stations for a small sample of data. The next chapter refers to the multivariable crisp and fuzzy linear regression. The eighth chapter deals with the fuzzy linear regression, with crisp input data and fuzzy output data. All the chapters offer a proper foundation of either widely used or new techniques upon regression. Among the new techniques, several innovated fuzzy regression based methodologies are developed for real problems, and useful conclusions are drawn"-- |c Provided by publisher. | ||
| 588 | |a Description based on print version record and CIP data provided by publisher; resource not viewed. | ||
| 505 | 0 | |a 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. | |
| 650 | 0 | |a Engineering mathematics. |0 http://id.loc.gov/authorities/subjects/sh85043235 | |
| 650 | 0 | |a Fuzzy statistics. |0 http://id.loc.gov/authorities/subjects/sh2004000270 | |
| 650 | 0 | |a Regression analysis. |0 http://id.loc.gov/authorities/subjects/sh85112392 | |
| 650 | 6 | |a Mathématiques de l'ingénieur. | |
| 650 | 6 | |a Statistique floue. | |
| 650 | 6 | |a Analyse de régression. | |
| 650 | 7 | |a MATHEMATICS / Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS / Probability & Statistics / General. |2 bisacsh | |
| 650 | 7 | |a Engineering mathematics |2 fast | |
| 650 | 7 | |a Fuzzy statistics |2 fast | |
| 650 | 7 | |a Regression analysis |2 fast | |
| 700 | 1 | |a Hrissanthou, Vlassios, |e editor. | |
| 700 | 1 | |a Spiliotis, Mike, |e editor. | |
| 758 | |i has work: |a Conventional and Fuzzy Regression (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYf3qbGY3F4Bf76j4vbtDq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Conventional and fuzzy regression |d Hauppauge, New York : Nova Science Publishers, Inc., [2018] |z 9781536137989 |w (DLC) 2018025725 |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-on1041247116 |
|---|---|
| _version_ | 1816882463161450496 |
| adam_text | |
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| author2 | Hrissanthou, Vlassios Spiliotis, Mike |
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| author_facet | Hrissanthou, Vlassios Spiliotis, Mike |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | T - Technology |
| callnumber-label | TA340 |
| callnumber-raw | TA340 |
| callnumber-search | TA340 |
| callnumber-sort | TA 3340 |
| callnumber-subject | TA - General and Civil Engineering |
| collection | ZDB-4-EBA |
| contents | 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. |
| ctrlnum | (OCoLC)1041247116 |
| dewey-full | 519.5/36 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 |
| dewey-search | 519.5/36 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1041247116 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:29:00Z |
| institution | BVB |
| isbn | 9781536137996 1536137995 |
| language | English |
| lccn | 2018028509 |
| oclc_num | 1041247116 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource. |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Nova Science Publishers, Inc., |
| record_format | marc |
| series2 | Environmental science, engineering and technology |
| spelling | Conventional and fuzzy regression : theory and engineering applications / Vlassios Hrissanthou and Mike Spiliotis, editors. New York : Nova Science Publishers, Inc., [2018] 1 online resource. text txt rdacontent computer n rdamedia online resource nc rdacarrier Environmental science, engineering and technology Includes bibliographical references and index. "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 linear or nonlinear regression. The application example refers to the relationship between sediment transport rates on the one hand and stream discharge and rainfall intensity on the other hand. Second chapter refers to the crisp linear or nonlinear regression of six heavy metals between different soft tissues and shells of Telescopium telescopium and its habitat surface sediments. Third describes the crisp linear, multiple linear, nonlinear and Gaussian process regressions. The fourth is confronted with a classic regression model, named Geographically Weighted Regression (GWR), which constitutes a spatial statistics method. The fifth chapter regards fuzzy linear regression based on symmetric triangular fuzzy numbers. The sixth chapter treats fuzzy linear regression based on trapezoidal membership functions. The main application of this chapter concerns the dependence of rainfall records between neighboring rainfall stations for a small sample of data. The next chapter refers to the multivariable crisp and fuzzy linear regression. The eighth chapter deals with the fuzzy linear regression, with crisp input data and fuzzy output data. All the chapters offer a proper foundation of either widely used or new techniques upon regression. Among the new techniques, several innovated fuzzy regression based methodologies are developed for real problems, and useful conclusions are drawn"-- Provided by publisher. Description based on print version record and CIP data provided by publisher; resource not viewed. 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. Engineering mathematics. http://id.loc.gov/authorities/subjects/sh85043235 Fuzzy statistics. http://id.loc.gov/authorities/subjects/sh2004000270 Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Mathématiques de l'ingénieur. Statistique floue. Analyse de régression. MATHEMATICS / Applied. bisacsh MATHEMATICS / Probability & Statistics / General. bisacsh Engineering mathematics fast Fuzzy statistics fast Regression analysis fast Hrissanthou, Vlassios, editor. Spiliotis, Mike, editor. has work: Conventional and Fuzzy Regression (Text) https://id.oclc.org/worldcat/entity/E39PCYf3qbGY3F4Bf76j4vbtDq https://id.oclc.org/worldcat/ontology/hasWork Print version: Conventional and fuzzy regression Hauppauge, New York : Nova Science Publishers, Inc., [2018] 9781536137989 (DLC) 2018025725 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1924961 Volltext |
| spellingShingle | Conventional and fuzzy regression : theory and engineering applications / 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. Engineering mathematics. http://id.loc.gov/authorities/subjects/sh85043235 Fuzzy statistics. http://id.loc.gov/authorities/subjects/sh2004000270 Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Mathématiques de l'ingénieur. Statistique floue. Analyse de régression. MATHEMATICS / Applied. bisacsh MATHEMATICS / Probability & Statistics / General. bisacsh Engineering mathematics fast Fuzzy statistics fast Regression analysis fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85043235 http://id.loc.gov/authorities/subjects/sh2004000270 http://id.loc.gov/authorities/subjects/sh85112392 |
| title | Conventional and fuzzy regression : theory and engineering applications / |
| title_auth | Conventional and fuzzy regression : theory and engineering applications / |
| title_exact_search | Conventional and fuzzy regression : theory and engineering applications / |
| title_full | Conventional and fuzzy regression : theory and engineering applications / Vlassios Hrissanthou and Mike Spiliotis, editors. |
| title_fullStr | Conventional and fuzzy regression : theory and engineering applications / Vlassios Hrissanthou and Mike Spiliotis, editors. |
| title_full_unstemmed | Conventional and fuzzy regression : theory and engineering applications / Vlassios Hrissanthou and Mike Spiliotis, editors. |
| title_short | Conventional and fuzzy regression : |
| title_sort | conventional and fuzzy regression theory and engineering applications |
| title_sub | theory and engineering applications / |
| topic | Engineering mathematics. http://id.loc.gov/authorities/subjects/sh85043235 Fuzzy statistics. http://id.loc.gov/authorities/subjects/sh2004000270 Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Mathématiques de l'ingénieur. Statistique floue. Analyse de régression. MATHEMATICS / Applied. bisacsh MATHEMATICS / Probability & Statistics / General. bisacsh Engineering mathematics fast Fuzzy statistics fast Regression analysis fast |
| topic_facet | 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 |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1924961 |
| work_keys_str_mv | AT hrissanthouvlassios conventionalandfuzzyregressiontheoryandengineeringapplications AT spiliotismike conventionalandfuzzyregressiontheoryandengineeringapplications |