Nonlinear regression modeling for engineering applications : modeling, model validation, and enabling design of experiments First edition.

Author

• Rhinehart, R. Russell

Additional Author(s)

-

Publisher

Hoboken, New Jersey: John Wiley and Sons, Inc., 2016

Language

English

ISBN

9781118597972

Series

Subject(s)

• ENGINEERING-MATHEMATICAL MODELS
• REGRESSION ANALYSIS--MATHEMATICAL MODELS

Notes

• Includes bibliographical references and index (355-361)

. .

Abstract

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.

Physical Dimension

Number of Page(s)

1 online resource (xxxviii, 361 p.)

Dimension

-

Other Desc.

-

Summary / Review / Table of Content

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.

Exemplar(s)

#	Accession No.	Call Number	Location	Status
1.	01055/19	620.00151953 Rhi N	Online !	Available