Nonlinear regression modeling for engineering applications : modeling, model validation, and enabling design of experiments / R. Russell Rhinehart.

Format:

Book

Author/Creator:

Rhinehart, R. Russell, 1946- author.

Series:

Wiley-ASME Press Series

Language:

English

Subjects (All):

Regression analysis--Mathematical models.

Regression analysis.

Engineering--Mathematical models.

Engineering.

Physical Description:

1 online resource (403 p.)

Edition:

First edition.

Place of Publication:

Chichester, England : Wiley : ASME Press, 2016.

Language Note:

English

Summary:

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.

Contents:

Cover; Title Page; Copyright; Contents; Series Preface; Preface; Acknowledgments; Nomenclature; Symbols; Part I Introduction; Chapter 1 Introductory Concepts; 1.1 Illustrative Example-Traditional Linear Least-Squares Regression; 1.2 How Models Are Used; 1.3 Nonlinear Regression; 1.4 Variable Types; 1.5 Simulation; 1.6 Issues; 1.7 Takeaway; Exercises; Chapter 2 Model Types; 2.1 Model Terminology; 2.2 A Classification of Mathematical Model Types; 2.3 Steady-State and Dynamic Models; 2.4 Pseudo-First Principles-Appropriated First Principles; 2.5 Pseudo-First Principles-Pseudo-Components

2.6 Empirical Models with Theoretical Grounding2.7 Empirical Models with No Theoretical Grounding; 2.8 Partitioned Models; 2.9 Empirical or Phenomenological?; 2.10 Ensemble Models; 2.11 Simulators; 2.12 Stochastic and Probabilistic Models; 2.13 Linearity; 2.14 Discrete or Continuous; 2.15 Constraints; 2.16 Model Design (Architecture, Functionality, Structure); 2.17 Takeaway; Exercises; Part II Preparation for Underlying Skills; Chapter 3 Propagation of Uncertainty; 3.1 Introduction; 3.2 Sources of Error and Uncertainty; 3.3 Significant Digits; 3.4 Rounding Off

3.5 Estimating Uncertainty on Values3.6 Propagation of Uncertainty-Overview-Two Types, Two Ways Each; 3.7 Which to Report? Maximum or Probable Uncertainty; 3.8 Bootstrapping; 3.9 Bias and Precision; 3.10 Takeaway; Exercises; Chapter 4 Essential Probability and Statistics; 4.1 Variation and Its Role in Topics; 4.2 Histogram and Its PDF and CDF Views; 4.3 Constructing a Data-Based View of PDF and CDF; 4.4 Parameters that Characterize the Distribution; 4.5 Some Representative Distributions; 4.6 Confidence Interval; 4.7 Central Limit Theorem; 4.8 Hypothesis and Testing

4.9 Type I and Type II Errors, Alpha and Beta4.10 Essential Statistics for This Text; 4.11 Takeaway; Exercises; Chapter 5 Simulation; 5.1 Introduction; 5.2 Three Sources of Deviation: Measurement, Inputs, Coefficients; 5.3 Two Types of Perturbations: Noise (Independent) and Drifts (Persistence); 5.4 Two Types of Influence: Additive and Scaled with Level; 5.5 Using the Inverse CDF to Generate n and u from UID(0, 1); 5.6 Takeaway; Exercises; Chapter 6 Steady and Transient State Detection; 6.1 Introduction; 6.2 Method; 6.3 Applications; 6.4 Takeaway; Exercises

Part III Regression, Validation, DesignChapter 7 Regression Target - Objective Function; 7.1 Introduction; 7.2 Experimental and Measurement Uncertainty-Static and Continuous Valued; 7.3 Likelihood; 7.4 Maximum Likelihood; 7.5 Estimating x and y Values; 7.6 Vertical SSD-A Limiting Consideration of Variability Only in the Response Measurement; 7.7 r-Square as a Measure of Fit; 7.8 Normal, Total, or Perpendicular SSD; 7.9 Akaho's Method; 7.10 Using a Model Inverse for Regression; 7.11 Choosing the Dependent Variable; 7.12 Model Prediction with Dynamic Models

7.13 Model Prediction with Classification Models

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781523154876

152315487X

9781118597958

1118597958

9781118597934

1118597931

9781118597972

1118597974

OCLC:

956648059