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Belz, Julian: Fighting the curse of dimensionality with local model networksBekämpfung des Fluchs der Dimensionalität mit lokalen Modellnetzen. 2018
Content
Titelblatt
Acknowledgments
Contents
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Kurzfassung
Abstract
1 Introduction
1.1 Motivation
1.2 Objectives and Structure of this Thesis
2 Nonlinear System Identification
2.1 Static and Dynamic Models
2.2 Curse of Dimensionality and Bias/Variance Tradeoff
2.3 Local Model Networks
2.4 Input Selection
2.5 Design of Experiments
2.6 Metamodeling
2.7 Static Function Generator
3 Input Selection Using Local Model Networks
3.1 Test Processes
3.2 Mixed Wrapper-Embedded Input Selection Approach
3.3 Regularization-Based Input Selection Approach
3.4 Embedded Approach
3.5 Visualization: Partial Dependence Plots
4 Design of Experiments Studies
4.1 Order Of Experimentation
4.2 Advisability of Specific Experimental Designs
4.3 Goal-Oriented Active Learning with Local Model Networks
5 Applications
5.1 Miles Per Gallon Data Set
5.2 Air-Mass Flow Prediction
5.3 Fan Metamodeling
5.4 Heating, Ventilating, and Air Conditioning System
6 Conclusions and Outlook
Conclusions
Outlook
A Data Splitting
References