Parameter Estimation for Complex Systems
Parameter Estimation for Complex Systems
Kappel, Franz; Banks, H. T.
Taylor & Francis Ltd
12/2023
448
Mole
Inglês
9781138105263
Descrição não disponível.
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
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Complex Systems;Experimental Design;Modeling;Parameter estimation;Statistics;Stochastic Modeling
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
