There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data. This work addresses model induction from data for the simulation of hydrodynamic processes in the aquatic environment.
There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data. This work addresses model induction from data for the simulation of hydrodynamic processes in the aquatic environment.
YONAS BERHAN DIBIKE born in Addis Ababa, Ethiopia Master of Science with Distinction, IHE.
Inhaltsangabe
Chapter 1 Introduction 1.1 Current Practices of Computational Hydraulic Modelling 1.2 Problems Associated with the Current Practice 1.3 Model Induction from Data: an Alternative Approach 1.4 Model Structure Selection 1.5 Outline of the Thesis Chapter 2 Artificial Neural Networks as Model Induction Techniques Chapter 3 Model Induction with Support Vector Machines Chapter 4 Artificial Neural Networks as Domain knowledge Encapsulators Chapter 5 Simulation of Hydrodynamic Processes Using ANNs Chapter 6 Developing Generic Hydrodynamic Models Using ANNs Chapter 7 Summary and Conclusions.
Chapter 1 Introduction 1.1 Current Practices of Computational Hydraulic Modelling 1.2 Problems Associated with the Current Practice 1.3 Model Induction from Data: an Alternative Approach 1.4 Model Structure Selection 1.5 Outline of the Thesis Chapter 2 Artificial Neural Networks as Model Induction Techniques Chapter 3 Model Induction with Support Vector Machines Chapter 4 Artificial Neural Networks as Domain knowledge Encapsulators Chapter 5 Simulation of Hydrodynamic Processes Using ANNs Chapter 6 Developing Generic Hydrodynamic Models Using ANNs Chapter 7 Summary and Conclusions.
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