Parameter Estimation for Complex Systems
1st Edition
Published on 31. December 2023
Book
Paperback/Softback
448 pages
978-1-138-10526-3 (ISBN)
Description
This book on parameter estimation addresses problems arising for comprehensive models of complex systems in various areas of application under data constraints. Paramater estimation is a branch of statistics that is used to estimate statistical results using data collected under set parameters with a random compenent. Here the authors present models demonstrating particular problems, proposed solutions and applications to illustrate addressing these problems.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Illustrations
150 s/w Abbildungen
150 Illustrations, black and white
Dimensions
Height: 229 mm
Width: 152 mm
ISBN-13
978-1-138-10526-3 (9781138105263)
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Schweitzer Classification
Content
Chapter 1: Introduction: Important problems encountered in modeling complex systems with data acquisition constraints.
Chapter 2: Formulations of the vector parameter estimation problems.
Chapter 3: Sensitivity Analysis
Chapter 4: Verification and Validation
Chapter 5: Aggregate Data - Aggregate model theory
Chapter 6: Model Discrepancy
Chapter 7: Identifiability, Parameter Redundancy, Methods for Parameter Correlation Investigation
Chapter 8: Information Content in Data Sets
Chapter 9: Optimal Experimental Design
Chapter 10: Generalized Sensitivities
Chapter 11: Parameter Subset Selection (PSS)
Chapter 12: Nonlinear Filtering
Chapter 13: Stochastic Nonlinear Mixed Effects Modeling
Chapter 14: Parameter Identification Using Neural Networks
Chapter 15: Control Issues
Chapter 2: Formulations of the vector parameter estimation problems.
Chapter 3: Sensitivity Analysis
Chapter 4: Verification and Validation
Chapter 5: Aggregate Data - Aggregate model theory
Chapter 6: Model Discrepancy
Chapter 7: Identifiability, Parameter Redundancy, Methods for Parameter Correlation Investigation
Chapter 8: Information Content in Data Sets
Chapter 9: Optimal Experimental Design
Chapter 10: Generalized Sensitivities
Chapter 11: Parameter Subset Selection (PSS)
Chapter 12: Nonlinear Filtering
Chapter 13: Stochastic Nonlinear Mixed Effects Modeling
Chapter 14: Parameter Identification Using Neural Networks
Chapter 15: Control Issues