Nonlinear Regression Modeling for Engineering Applications Modeling Model Validation and Enabling Design of Experiments 1st Edition by R Russell Rhinehart ISBN 9781118597965 1118597966

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Product details:

ISBN 10:  1118597966

ISBN 13: 9781118597965

Author:  R. Russell Rhinehart

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.

Table of contents: 

Part I INTRODUCTION

1 Introductory Concepts

2 Model Types

Part II PREPARATION FOR UNDERLYING SKILLS

3 Propagation of Uncertainty

4 Essential Probability and Statistics

5 Simulation

6 Steady and Transient State Detection

Part III REGRESSION, VALIDATION, DESIGN

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

Part IV CASE STUDIES AND DATA

21 Case Studie

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Tags: R Russell Rhinehart, Nonlinear Regression, Modeling, Engineering Applications, Model Validation, Enabling, Design, Experiments