Nonlinear Regression Modeling for Engineering Applications - Modeling, Model Validation, and Enabling Design of Experiments

Name: Nonlinear Regression Modeling for Engineering Applications - Modeling, Model Validation, and Enabling Design of Experiments
Brand: John Wiley & Sons Inc
SKU: 9781118597965
Price: 135.47 EUR
Availability: InStock

Автор: R. Russell Rhinehart

Език:

Корици: С твърди корици

Издател: John Wiley & Sons Inc

Наличност: Външен склад в ограничено количество

Изпращаме след 11-15 дни

135.47 € 264.96 лв

Since mathematical models express our understanding of how nature behaves, we use them to validate o...

Информация за книгата

Автор

R. Russell Rhinehart

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2016

страници

400

EAN

9781118597965

ISBN

9781118597965

Enbook ID

10045768

Издател

John Wiley & Sons Inc

Теглоt

760

Размери

253 x 180 x 23

Пълно описание

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.

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