1. Available courses - Department of Cybernetics
General information: due to a small number of students attending courses taught in English,
the courses bellow are offered on an individual basis, that means as consultations,
self-work and/or reports.
Adaptive Control
The adaptive control problem, parameter identification, deterministic and stochastic self – tuning regulators, model-
reference adaptive systems, gain scheduling, properties of adaptive systems, dual control, stochastic optimal control,
multiple model adaptive control, adaptive control and artificial intelligence, intelligent adaptive control.
References :
[1] Šimandl M.: Adaptivní systémy. Skriptum, ZČU v Plzni, the 3rd addition, 2001
[2] Åström K. J.- Wittenmark B.: Adaptive Control, Addison-Wesley, Second Edition, 1995
[3] Landau I.D.- Lozano R.- M´Saad M.: Adaptive Control, Springer, 1997
[4] Mosca E.: Optimal, Predictive, and Adaptive ontrol, Prentice Hall, New Jersey,1995
[5] Fabri S.G.-Kadirkamanathan V.: Functional Adaptive Control, Springer-Verlag London, 2001
Artificial Intelligence
Automated reasoning with propositional logic, automated reasoning with predicate logic, substitutions and unifiers.
Knowledge representation. Productions systems. Semantic networks, frames and scripts. Expert systems. Solving
problems by searching state space. Solving problems by searching decompositions. Planning, GPS, STRIPS and
PLANNER. Playing games by searching trees, minimax procedure, alphabeta in minimax, pruning. Pattern recognition.
Machine learning. Neural nets. Genetic programming. Intelligent agents. Qualitative modeling. Machine perception.
Natural Language processing.
References
[1] Mařík,V. a kol.: Umělá inteligence I., II., III. Academia, Praha 1993 (1999, 2001).
[2] Nilsson, N., J.: Artificial Intelligence – A New Synthesis. Morgan Kaufmann Publishers, San
Francisco 1998.
[3] Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, 1995.
Computational Linguistics
Practical application of basic methods and algorithms of text processing for Natural Language Processing applications in
general. Special attention will be given to Czech as a inflective language. Students will work on one-semester long
projects.
References:
[1] Petkevič, Vladimír (ed).: Korpusová lingvistika. Překlad článků oboru korpusová
lingvistika. FF UK. 2000.
[2] Charniak, E.: Statistical Language Learning. The MIT Press. 1996. ISBN 0-262-53141-0.
[3] Wall, L., Christiansen, T. and R. L. Schwartz: Programming PERL, 3rd ed.. O'Reilly.
1996. ISBN 0-596-00027-8.
Control Theory
Control theory and controller design problem. Stochastic control system, its optimality and optimization recursion.
Bellman's optimization recursion, state-space feedback. LQG problem, linear tracking problem. Deterministic control
system, minimum principle. Coherences with game theory. Theory and experience.
References:
[1] Andeson B.D.O. – Moore J.B.: Optimal Control: Linear Quadratic Metods. Prentice-Hall, Inc.,
Englewood Clifs, NJ, 1989
[2] Ĺström K.J., Wittenmark B.: Computer-Controlled Systems. Prentice-Hall, Inc., Upper Saddl
River, NJ, 1997
[3] Bertsekas D.P.: Dynamic Programming and Optimal Control. Albena Scietific, Belmont,
Massachusetts 1995
[4] Kučera V.: Analysis and Design of Discrete Linear Control Systems. Csechoslovak Acadamy of
Sciences, Prague 1991
[5] Žampa P., Mošna J., Prautch P. A New Approach to Optimal Control Theory. In The 2nd IFAC

2. Workshop on New Trends in Design of Control Systems, Smolenice, Slovac Republic, 1997, p.122-127
Decentralized and hierarchical control
Introduction to system complexity and large-scale system control. Large-scale system model reduction methods -
aggregation, perturbation, power series expansion and decomposition methods. Control design based on reduced models.
Multivariable systems - description, properties, multivariable feedback design. Interactive and non-interactive control,
design of decoupling controllers. Large-scale systems control under structural constraints on information and control
system. Decentralized control and stabilization with local dynamic and non-dynamic controllers. Decentralized fixed
modes - characterization, existence criteria, removing of fixed modes. Near-optimum decentralized control design with
local dynamic and non-dynamic controllers. Hierarchical multilevel control, coordinating control from higher hierarchical
level. Principles of coordination - model and goal coordination method. Static and dynamic optimization in two-level
hierarchical control.
References:
[1] Jamshidi M.: Large-Scale Systems. Modeling and Control. North-Holland series insystem science
and engineering, vol.9. New York 1983.
[2] Maciejowski J.M.: Multivariable Feedback Design. Addison-Wesley Publishers, Wokingham 1989.
[3] Trave L., Titli A., Tarras A.: Large Scale Systems: Decentralization, Structure Constraints and
Fixed Modes. Lecture notes in control and information sciences, vol. 120. Springer Verlag, Berlin
1989.
[4] Vardulakis A.I.G.: Linear Multivariable Control. John Willey, N.Y. 1991
Digital Image Processing
Pre-processing, understanding, computer vision. Colour information representation and processing. Point-to-point
transformations, geometric transformations, local pre-processing, image restoration. Mathematical morphology.
Segmentation by thresholding, edge detection, region growing, template matching. Object description, region
identification, edge-based
description, region-based description. Recognition, structural and feature-based recognition. Motion analysis, differential
method, optical flow, pixel correspondence. 3D vision, “shape form X”, 3D objects modelling. Applications – remote
sensing, industry, medicine. File formats, compression algorithms, loss and lossless compression. Image processing
hardware, TV camera and CCD, frame grabbers.
References:
[1] Šonka M., Hlaváč V., Boyle R.: Image Processing, Analysis and Machine Vision, Chapman & Hall,
1993.
[2] Rosenfeld A., Kak A. C.: Digital Image Processing, Academic Press, 1982
[3] Jaroslavskij L., Bajla I.: Metódy a systémy číslicového spracovania obrazov, Alfa, 1989.
General Systems Theory
State space system theory: axiomatic foundations, objective and subjective system models, discrete-time stochastic
systems, stochastic difference equations, Wiener-Lčvy stochastic process, diffusion systems, stochastic differential
equations, stability and quality of stochastic systems, system structure, modeling and simulation of stochastic systems,
cybernetic systems, estimation and control systems, role of adaptability and artificial intelligence in cybernetics
References:
Zadeh, L. A., Desoer C. A.: Linear System Theory. McGraw-Hill Book Company, New York. Kwakernaak H.,
Sivan R.: Modern Signals and Systems.. Prentice Hall, Inc. Englewood Cliffs, New Jersey 1991
Information and Control Systems
Design of automated information and control systems (ICS). System analysis methods structured object-oriented
Structure and data analysis. State methods and sequential functions charts. Computer aided system analysis. Project life
cycle. CASE systems, SW-Tools. Computer aided analysis and design of ICS. Complex design methods for industrial
ICS.

3. References:
[1] Cendelín J.: Informační a řídicí systémy. Vydavatelství ZČU, Plzeň 1999
[2] Douglas P. B.: Real-Time UML, Addison- Wesley, 1998.
[3] Balzert H.: CASE, Auswahl, Einfuhrung, Erfahrungen. BI Wissenschaftsverlag, 1993.
Linear Systems
Descriptions of linear systems: the convolution, transfer function and state space representation. Basic concepts:
canonical forms, controllability, observability, minimal realizations, Markov’s parameters. Linear state, output and
dynamical feedback: the Brunovsky canonical form, Kronecker’s indices, Rosenbrock’s control structure theorem, pole
placement problems, optimal quadratic controllers. The asymptotic, Luenberger’s and Kalman’s observers. Structural
properties of linear systems. Polynomic approach to linear systems. Uncertain linear systems: interval systems, systems
defined by a few samples of a frequency response, Pick’s and Bochner’s theorems.
References :
[1] Kailath T.: Linear Systems. Prentice-Hall. Inc., Englewood Cliffs, N.J., 1980.
[2] Zadeh L.A.- Desoer C.A.: Linear Systém Theory – A State Space Approach, McGraw-Hill,
New York, 1963.
[3] Rosenbrock H.H.: State Space and Multivariable Theory. Wiley, New York, 1970.
[4] Kučera V.: Discrete Linear Control. Wiley, Chichester U.K., 1979.
Knowledge-based and expert systems
Knowledge-based systems (KBS) and expert systems (ES) definition. The architecture of KBS. Distinctive features and
complementary roles of KBS and traditional software systems. The theoretical foundations of KBS: knowledge
representation – rules, semantic network, frames, logic; inference techniques: forward-chaining, backward-chaining,
search techniques, nonmonotonic inference. Inexact reasoning: Bayesian rules, certainty theory, fuzzy-logic, Dempster-
Shafer theory. Inductive acquisition of rules. Knowledge acquisition. KBS and traditional software systems development,
phases in KBS development and KBS life cycle. People involved in an expert system project. A description of the
MYCIN (ES system for meningitis and bacteremias). Hands-on development of a simple ES. ES development packages
will be used to create a rudimentary expert system.
References :
[1] Kepka J., Psutka J.: Umělá inteligence : expertní systémy. I. díl. Plzeň, ZČU v Plzni, 1994.
[2] Kepka J., Psutka J.: Umělá inteligence : reprezentace znalostí, Plzeň, ZČU v Plzni, 1994.
[3] Durkin J.: Expert Systems: Design and Development, Prentice Hall International Ed.1994.
[1] Giarratono and G Riley, Expert Systems: Principles and Programming, PWS Publishing Co., 1994.
Man-machine communication by speech
The state-of-the-art man-computer communication. Production model of speech processing. Speech signal processing in
time and frequency domain. Discrete Fourier transformation, cepstral and LPC analysis. Model of hearing, mel-frequency
analysis, perceptual linear predictive analysis. Pith period detection, formant frequencies estimation, phonetic
transcription. Vector quantization. Speech synthesis, synthesis in time and frequency domain. Text-to speech synthesis.
Prosody. Isolated words recognition, dynamic programming. Statistical approach to speech recognition, Hidden Markov
models. Acoustic and language modeling. Decoding. Key words spotting. Speaker adaptation. Identification and
verification of a speaker. Speech understanding. Dialog systems.
References:
[1] Psutka, J.: Komunikace s počítačem mluvenou řečí. Academia, Praha 1995.
[2] Jurafsky, D., Martin, J., M.: Speech and Language processing. Prentice-Hall, Upper Saddle River
New Jersey 2000.
[3] Young, S., Bloothooft, G.: Corpus-based Methods in Language and Speech Processing. Kl
Academic Publishers 1997.
[4] Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge 1997.

4. Modeling and Simulation
Object, model, system, simulation. System analysis, object-oriented approach. Computer based modeling of continuous a
discrete events system. Object-oriented programming and simulation. Programming language Simula. Digital simulation
systems. OO CASE systems.
References:
[1] Cendelín J, Kindler E.: Modelování a simulace. Vydavatelství ZČU, Plzeň 1994.
[2] Zítek P.: Simulace dynamických systémů. SNTL, Praha 1990.
[3] Hušek R., Lauber J.: Simulační modely,SNTL, 1987.
Neural Networks
Models of neural networks. Multilayer perceptron networks, recurrent networks. Algorithms for neural networks learning.
Supervised learning, unsupervised learning. Algorithm backpropagation, modifications. Associative memories. Hopfield
network, Boltzman machine. Elman neural network. Self-organising networks. Kohonen network, Kohonen maps. LVQ
(Learning Vector Quantization) networks. Adaptive resonant networks. RBF (Radial Basis Function) networks.
Counterpropagation networks. Applications of neural networks. Neural networks for signal processing. Neural networks
for pattern recognition.
References:
[1] Kvasnička V., Beňušková Ľ., Pospíchal J., Farkaš I., Tiňo P., Kráľ A.: Úvod do teórie neurónový
sietí. IRIS, Bratislava, 1997.
[2] Luo F.-L., Unbehauen R.: Applied Neural Networks for Signal Processing. Cambridge University
Press, Cambridge 1997.
[3] Novák M. a kol.: Umělé neuronové sítě. Teorie a aplikace. Nakladatelství C.H.Beck, Praha 1998
[4] Zurada J.M.: Introduction to Artificial Neural Systems. West Publishing Company, St.
Paul, 1992.
Pattern Recognition
Feature based methods of recognition objects. Minimum-distance, NN and k-NN classifiers. Trainable pattern classifiers,
perceptron, linear discriminant function. Stochastic approximation methods, problem of convergence. Pattern
classification as a statistical decision problem, Bayes classifier. Estimation of probability density functions. Unsupervised
pattern recognition, measures of similarity, clustering criteria, k-means alg., Isodata alg. Pattern processing, feature
extraction and selection, orthogonal expansion, divergence maximization. Syntactic pattern recognition. Types of
grammars, formulation of the syntactic pattern recognition problem, grammars and automata. Stochastic grammars and
languages. Learning and grammatical inference, automata as pattern recognizers.
References:
[1] Kotek, Z., Mařík, V., Hlaváč, V., Psutka, J., Zdráhal, Z.: Metody rozpoznávání a jejich aplikace.
Academia, Praha 1993.
[2] Kepka, J., Psutka, J.: Strukturální metody rozpoznávání. Vysokoškolská skripta ZČU v Plzni. Ediční středisko ZČU,
Plzeň 1993. 205s.
[3] Kepka, J., Psutka, J.: Kombinace příznakových a strukturálních metod rozpoznávání. Vysokoškolská skripta
ZČU v Plzni. Ediční středisko ZČU, Plzeň 1994. 80s.
[4] Duda, R.,O., Hart, P.,E.: Pattern Classification and Scene Analysis. John Wiley&Sons, New York, 1973.
[5] Tou, J., T., Gonzalez, R., C.: Pattern Recognition Principles. Addison-Wesley Publishing Company, London 1974.
Robust Control
The course deals with the problem of robust stability and robust controller design. An uncertain system is defined as a
family of linear time-invariant systems. A property is called robust if it is satisfied for all members of the family.
Similarly, a controller is called robust if satisfies all design specifications for all considered systems. Contents: uncertain
linear systems, structured and unstructured uncertainty problems, robust stability, Kharitonov’s theorem and
modifications, the value set concept, polytopes of polynomials, Edge theorem, multilinear uncertainty structures, the
special types of uncertainty structures, design of robust controller, the H ∞ method, non-convex optimization.
References:

5. [1] Barmish B. R.: New Tools for Robustness of Linear Systems. Macmillan Publishing Company,
1994.
[2] Bhattacharyya S. P. – Chapellat H. – Keel L. H.: Robust Control: the Parametric Approach. Prentice
Hall Inc., 1995.
[3] Kwakernaak H.: Robust Control and H ∞ - Optimization Tutorial Paper. Automatica, Vol.29, No 2,
pp.255-273, 1993.
Signal Processing
The Methods of Time Series Analysis, Time-Series Models, Least –Squares Methods, Regression Analysis, Fourier
methods, Discrete Fourier Transform, Time Series Parameter Estimation, Optimal Prediction, Filtering, Smoothing,
Adaptive Prediction, Adaptive Filtering, Fault Detection, Nonlinear Filtering
References:
[1] Šimandl M.: Adaptivní systémy. Skriptum, ZČU v Plzni, třetí vydání, 2001
[2] Pollock D.S.G.: A Handbook of Time-Series Analysis, Signal Processing and Dynamics, Academic
Press, London, 1999
[3] West M.- Harrison J.: Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York,
1997
[4] Widrow B.- Stearns S.D.: Adaptive Signal Processing, Englewood Cliffs, N.J.: Prentice Hall,1985
[5] Šimandl M.: Identifikace systémů a filtrace. Skriptum, ZČU v Plzni, třetí vydání, 200.
System Identification and Nonlinear Filtering
System identification, system, model structure, experimental condition, identification methods, recursive identification
techniques, prediction error method, instrumental variable method, parameter tracking, model selection and verification
of model validity. Linear filtering, Wiener and Kalman filter, nonlinear filtering, extended Kalman filter, iteration filter,
second order filter, Bayesian approach, global filters, point mass method, Gaussian sum method, Monte Carlo techniques,
Cramér-Rao bound.
References:
[1] Šimandl M.: Identifikace systémů a nelineární filtrace. Skriptum, ZČU v Plzni, třetí vydání, 2001
[2] Ljung L: System Identification. Theory for the User. Prentice-Hall, Englewood Cliffs, 1987
[3] Liu J. S.: Monte Carlo Strategies in Scientific Computing. Springer-Verlag, New York, 2001
[4] Jazvinski A.H..: Stochastic processes and filtering theory, Academic Press, New York, 1970
[5] Anderson B. – Moore J..: Optimal Filtering. Prentice Hall, New Jersey, 1979
Statistical Natural Language Processing
Introductory natural language processing course with broad coverage of fundamental probabilistic and statistical methods.
Basic notions from NLP are also explained (morphology, tagging, statistical parsing, textual corpora and their usage,
language modeling and introduction to general linguistics for computer scientists). Seminars will be devoted to
independent individual work on projects related to the material explained in the lectures.
References:
[1] Manning, C. D. and H. Schütze: Foundations of Statistical Natural Language
Processing. The MIT Press. 1999. ISBN 0-262-13360-1.
[2] Jurafsky, D. and J. H. Martin: Speech and Language Processing. Prentice-Hall. 2000.
ISBN 0-13-095069-6.
[3] Allen, J.: Natural Language Understanding. The Benajmins/Cummings Publishing
Company. Inc. 1994. ISBN 0-8053-0334-0.
Stochastic models of operation research
Decision proces, optimization problems, linear programming, duality, the simplex method. Integer linear programming.
Game theory, games in normal form, matrix games, diferential games. Markov chain, regular and absorbtion process.

6. Queuing theory, arrival stochastic process, Poisson proces, markov model of service system, opened and closed service
systems.
Basic models of project planing. Resposibility.
References:
[1] Kleinrock L.: Queueing Systems. Wiley Interscience, New York, 1975
[2] Maňas M.: Teorie her a optimálního rozhodování. SNTL, 1974
[3] Mitrani I.: Modelling of computer and communication systems. Cambridge University press,
Cambridge, 1987
[4] Mošna J. – Pešek P.: Operační analýza, studijní text KKY ZČU, Plzeň 2001, s. 128, elektronická
verze, http://control.zcu.cz/~pesek/OA2000/user/podklady.php3
[2] Shogan A. W.: Management Science. Prentice-Hall Inc., 1988.
[3] Maňas Miroslav. Teorie her a její aplikace : Vysokošk. učebnice pro stud. VŠE v Praze i stud. Ostatních ekon. fakult
jiných vys. škol. Praha : Státní nakladatelství technické literatury, 1991 (univ. knihovna)
[4] Mas-Colell Andreu, Whinston Michael D., Green Jerry R. Microeconomic Theory. New York : Oxford University
Press, 1995. (univ. knihovna).
Theory of state estimation of stochastic systems
Introduction to modern stochastic systems theory, formulation of the general problem of state estimation of stochastic
systems, theory of state estimation of discrete-time and continuous-time linear systems, structure, properties and
application of Kalman filter, systems identification, problems of estimation in closed-loop automatic control systems
References:
Anderson B. D. O., J. B. Moore: Optimal Filtering. Englewood Cliffs, New Jersey, Prentice Hall, Inc. 1979
Jazwinski, A. H.: Stochastic Processes and Filtering Theory. Academic Press, New York, 1970
Goodwin G. C., S. F. Graebe, M.E. Salgado: Control System Design, Upper Saddle River, New Jersey, Prentice
Hall, Inc. 2001
Žampa P.: Teorie odhadování ve stochastických systémech. Studijní texty, ZČU Plzeň
http://control.zcu.cz/predmza.html