Introduction.- Part One Optimization.- Introduction to
Optimization.- Linear Optimization.- Nonlinear Local Optimization.-
Nonlinear Global Optimization.- Unsupervised Learning Techniques.-
Model Complexity Optimization.- Summary of Part 1.- Part Two Static
Models.- Introduction to Static Models.- Linear, Polynomial, and
Look-Up Table Models.- Neural Networks.- Fuzzy and Neuro-Fuzzy
Models.- Local Linear Neuro-Fuzzy Models: Fundamentals.- Local
Linear Neuro-Fuzzy Models: Advanced Aspects.- Input Selection for
Local Model Approaches.- Gaussian Process Models (GPMs).-
Summary of Part Two.- Part Three Dynamic Models.- Linear
Dynamic System Identification.- Nonlinear Dynamic System
Identification.- Classical Polynomial Approaches.-Dynamic Neural
and Fuzzy Models.- Dynamic Local Linear Neuro-Fuzzy Models.-
Neural Networks with Internal Dynamics.- Part Five Applications.-
Applications of Static Models.- Applications of Dynamic Models.-
Desing of Experiments.- Input Selection
Applications.- Applications of Advanced Methods.- LMN
Toolbox.- Vectors and Matrices.- Statistics.- Reference.-
Index.
Oliver Nelles was born in Frankfurt (Main), Germany, and got his Master’s and Ph.D. degree in Electrical Engineering and Automatic Control at the Technical University of Darmstadt. After being a Post-Doc at the Department of Mechanical Engineering at UC Berkeley he worked for Siemens VDO Automotive in Regensburg. During his five years in Regensburg he was project and group leader in the field of transmission control. Since 2004 he assumed a position as Professor for Automatic Control – Mechatronics at the University of Siegen. Oliver Nelles’ key research areas are: machine learning, system identification, nonlinear dynamic systems & control, design of experiments (DoE), fault diagnosis.
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