Suchergebnis: Katalogdaten im Herbstsemester 2016

Rechnergestützte Wissenschaften Master Information
Vertiefungsgebiete
Systems and Control
NummerTitelTypECTSUmfangDozierende
227-0103-00LRegelsysteme Information W6 KP2V + 2UF. Dörfler
KurzbeschreibungStudy of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.
LernzielStudy of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.
InhaltProcess automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation.
LiteraturK. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010.
R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007.
G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010.
J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014.
J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014.
Voraussetzungen / BesonderesPrerequisites: Signal and Systems Theory II.

MATLAB is used for system analysis and simulation.
227-0045-00LSignal- und Systemtheorie IW4 KP2V + 2UH. Bölcskei
KurzbeschreibungSignaltheorie und Systemtheorie (zeitkontinuierlich und zeitdiskret): Signalanalyse im Zeit- und Frequenzbereich, Signalräume, Hilberträume, verallgemeinerte Funktionen, lineare zeitinvariante Systeme, Abtasttheoreme, zeitdiskrete Signale und Systeme, digitale Filterstrukturen, diskrete Fourier-Transformation (DFT), endlich-dimensionale Signale und Systeme, schnelle Fouriertransformation (FFT).
LernzielEinführung in die mathematische Signaltheorie und Systemtheorie.
InhaltSignaltheorie und Systemtheorie (zeitkontinuierlich und zeitdiskret): Signalanalyse im Zeit- und Frequenzbereich, Signalräume, Hilberträume, verallgemeinerte Funktionen, lineare zeitinvariante Systeme, Abtasttheoreme, zeitdiskrete Signale und Systeme, digitale Filterstrukturen, diskrete Fourier-Transformation (DFT), endlich-dimensionale Signale und Systeme, schnelle Fouriertransformation (FFT).
SkriptVorlesungsskriptum, Übungsskriptum mit Lösungen.
227-0225-00LLinear System TheoryW6 KP5GM. Kamgarpour
KurzbeschreibungThe class is intended to provide a comprehensive overview of the theory of linear dynamical systems, their use in control, filtering, and estimation and their applications to areas ranging from avionics to systems biology.
LernzielBy the end of the class students should be comfortable with the fundamental results in linear system theory and the mathematical tools used to derive them.
Inhalt- Rings, fields and linear spaces, normed linear spaces and inner product spaces.
- Ordinary differential equations, existence and uniqueness of solutions.
- Continuous and discrete time, time varying linear systems. Time domain solutions. Time invariant systems treated as a special case.
- Controllability and observability, canonical forms, Kalman decomposition. Time invariant systems treated as a special case.
- Stability and stabilization, observers, state and output feedback, separation principle.
- Realization theory.
SkriptF.M. Callier and C.A. Desoer, "Linear System Theory", Springer-Verlag, 1991.
Voraussetzungen / BesonderesPrerequisites: Control Systems I (227-0103-00) or equivalent and sufficient mathematical maturity.
252-0535-00LMachine Learning Information W8 KP3V + 2U + 2AJ. M. Buhmann
KurzbeschreibungMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
LernzielStudents will be familiarized with the most important concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. A machine learning project will provide an opportunity to test the machine learning algorithms on real world data.
InhaltThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

- Bayesian theory of optimal decisions
- Maximum likelihood and Bayesian parameter inference
- Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
- Ensemble methods: Bagging and Boosting
- Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
- Non parametric density estimation: Parzen windows, nearest nieghbour
- Dimension reduction: principal component analysis (PCA) and beyond
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Voraussetzungen / BesonderesThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution.
151-0575-01LSignals and Systems Information W4 KP2V + 2UR. D'Andrea
KurzbeschreibungSignals arise in most engineering applications. They contain information about the behavior of physical systems. Systems respond to signals and produce other signals. In this course, we explore how signals can be represented and manipulated, and their effects on systems. We further explore how we can discover basic system properties by exciting a system with various types of signals.
LernzielMaster the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise.
InhaltDiscrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design.
SkriptLecture notes available on course website.
151-0563-01LDynamic Programming and Optimal Control Information W4 KP2V + 1UR. D'Andrea
KurzbeschreibungIntroduction to Dynamic Programming and Optimal Control.
LernzielCovers the fundamental concepts of Dynamic Programming & Optimal Control.
InhaltDynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
LiteraturDynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Voraussetzungen / BesonderesRequirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
401-5850-00LSeminar in Systems and Control for CSEW4 KP2SJ. Lygeros
Kurzbeschreibung
Lernziel
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