Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments
Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications relat...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, N.Y.
The American Society of Mechanical Engineers
[2016]
|
| Schriftenreihe: | Wiley-ASME Press series
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization. First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding. If the model does not fit the data, our understanding of the mechanism was wrong or incomplete. Patterns in the residuals can guide model improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications. This book details methods of nonlinear regression, computational algorithms, model validation, interpretation of residuals, and useful experimental design. The focus is on practical applications, with relevant methods supported by fundamental analysis. This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model. The author has used the material in the undergraduate unit operations lab course and in advanced control applications |
| Beschreibung: | Includes bibliographical references and index 1 Introductory Concepts -- 2 Model Types -- 3 Propagation of Uncertainty -- 4 Essential Probability and Statistics -- 5 Simulation -- 6 Steady and Transient State Detection -- 7 Regression Target - Objective Function -- 8 Constraints -- 9 The Distortion of Linearizing Transforms -- 10 Optimization Algorithms -- 11 Multiple Optima -- 12 Regression Convergence Criteria -- 13 Model Design - Desired and Undesired Model Characteristics and Effects -- 14 Data Pre- and Post-processing -- 15 Incremental Model Adjustment -- 16 Model and Experimental Validation -- 17 Model Prediction Uncertainty -- 18 Design of Experiments for Model Development and Validation -- 19 Utility versus Perfection -- 20 Troubleshooting -- 21 Case Studies. - System requirements: Adobe Acrobat Reader. - Mode of access: Internet via World Wide Web |
| Beschreibung: | 1 online resource (400 Seiten) illustrations |
| ISBN: | 9781118597958 1118597958 |
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| index_date | 2024-07-03T14:14:56Z |
| indexdate | 2024-07-10T08:50:06Z |
| institution | BVB |
| isbn | 9781118597958 1118597958 |
| language | English |
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| spelling | Rhinehart, R. Russell 1946- aut Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments R Russell Rhinehart Nonlinear Regression Modeling for Engineering Applications, Modeling, Model Validation, and Enabling Design of Experiments New York, N.Y. The American Society of Mechanical Engineers [2016] 1 online resource (400 Seiten) illustrations txt rdacontent c rdamedia cr rdacarrier Wiley-ASME Press series Includes bibliographical references and index 1 Introductory Concepts -- 2 Model Types -- 3 Propagation of Uncertainty -- 4 Essential Probability and Statistics -- 5 Simulation -- 6 Steady and Transient State Detection -- 7 Regression Target - Objective Function -- 8 Constraints -- 9 The Distortion of Linearizing Transforms -- 10 Optimization Algorithms -- 11 Multiple Optima -- 12 Regression Convergence Criteria -- 13 Model Design - Desired and Undesired Model Characteristics and Effects -- 14 Data Pre- and Post-processing -- 15 Incremental Model Adjustment -- 16 Model and Experimental Validation -- 17 Model Prediction Uncertainty -- 18 Design of Experiments for Model Development and Validation -- 19 Utility versus Perfection -- 20 Troubleshooting -- 21 Case Studies. - System requirements: Adobe Acrobat Reader. - Mode of access: Internet via World Wide Web Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization. First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding. If the model does not fit the data, our understanding of the mechanism was wrong or incomplete. Patterns in the residuals can guide model improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications. This book details methods of nonlinear regression, computational algorithms, model validation, interpretation of residuals, and useful experimental design. The focus is on practical applications, with relevant methods supported by fundamental analysis. This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model. The author has used the material in the undergraduate unit operations lab course and in advanced control applications TECHNOLOGY & ENGINEERING / Engineering (General) / bisach TECHNOLOGY & ENGINEERING / Reference / bisach Engineering / Mathematical models Regression analysis / Mathematical models Datenanalyse (DE-588)4123037-1 gnd rswk-swf Mathematische Modellierung (DE-588)7651795-0 gnd rswk-swf Ingenieurwissenschaften (DE-588)4137304-2 gnd rswk-swf Nichtlineare Regression (DE-588)4251077-6 gnd rswk-swf Nichtlineares Regressionsmodell (DE-588)4251078-8 gnd rswk-swf Electronic books Nichtlineares Regressionsmodell (DE-588)4251078-8 s Mathematische Modellierung (DE-588)7651795-0 s Ingenieurwissenschaften (DE-588)4137304-2 s Nichtlineare Regression (DE-588)4251077-6 s Datenanalyse (DE-588)4123037-1 s DE-604 The American Society of Mechanical Engineers Sonstige oth Erscheint auch als Druck-Ausgabe 9781118597965 https://asmedigitalcollection.asme.org/ebooks/book/52/Nonlinear-Regression-Modeling-for-Engineering Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Rhinehart, R. Russell 1946- Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments TECHNOLOGY & ENGINEERING / Engineering (General) / bisach TECHNOLOGY & ENGINEERING / Reference / bisach Engineering / Mathematical models Regression analysis / Mathematical models Datenanalyse (DE-588)4123037-1 gnd Mathematische Modellierung (DE-588)7651795-0 gnd Ingenieurwissenschaften (DE-588)4137304-2 gnd Nichtlineare Regression (DE-588)4251077-6 gnd Nichtlineares Regressionsmodell (DE-588)4251078-8 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)7651795-0 (DE-588)4137304-2 (DE-588)4251077-6 (DE-588)4251078-8 |
| title | Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments |
| title_alt | Nonlinear Regression Modeling for Engineering Applications, Modeling, Model Validation, and Enabling Design of Experiments |
| title_auth | Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments |
| title_exact_search | Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments |
| title_exact_search_txtP | Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments |
| title_full | Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments R Russell Rhinehart |
| title_fullStr | Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments R Russell Rhinehart |
| title_full_unstemmed | Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments R Russell Rhinehart |
| title_short | Nonlinear Regression Modeling for Engineering Applications |
| title_sort | nonlinear regression modeling for engineering applications modeling model validation and enabling design of experiments |
| title_sub | Modeling, Model Validation, and Enabling Design of Experiments |
| topic | TECHNOLOGY & ENGINEERING / Engineering (General) / bisach TECHNOLOGY & ENGINEERING / Reference / bisach Engineering / Mathematical models Regression analysis / Mathematical models Datenanalyse (DE-588)4123037-1 gnd Mathematische Modellierung (DE-588)7651795-0 gnd Ingenieurwissenschaften (DE-588)4137304-2 gnd Nichtlineare Regression (DE-588)4251077-6 gnd Nichtlineares Regressionsmodell (DE-588)4251078-8 gnd |
| topic_facet | TECHNOLOGY & ENGINEERING / Engineering (General) / bisach TECHNOLOGY & ENGINEERING / Reference / bisach Engineering / Mathematical models Regression analysis / Mathematical models Datenanalyse Mathematische Modellierung Ingenieurwissenschaften Nichtlineare Regression Nichtlineares Regressionsmodell |
| url | https://asmedigitalcollection.asme.org/ebooks/book/52/Nonlinear-Regression-Modeling-for-Engineering |
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