Modern Means in Automation
FEKT-BMPAAcad. year: 2015/2016
The course is focused on the use of knowledge systems in automation. In this context, explains the concepts of data, information and knowledge. The lectures are focused on the issue of expert systems, artificial neural networks, creation and solution innovation award and computer vision.
Learning outcomes of the course unit
Course graduate should be able to:
- explain the differences between the concepts of data, information and knowledge,
- explain the architecture and functionality of expert systems,
- create a base of knowledge for expert system NPS32,
- choose the field of application of expert systems,
- explain the paradigm of multilayer neural networks with backpropagation learning,
- discuss the settings for a parameter, the neural network,
- apply features multi-layered neural network backpropagation learning,
- design own solution of optimization task based on genetic algorithms,
- explain and use analysis of an object, formulation of innovative tasks to be solved, formulation of inventive tasks and use recommendation how to find new solutions,
- apply optical information in technical equipment.
The subject knowledge on the secondary school level is required.
Recommended optional programme components
Recommended or required reading
Kasabov,N.K.: Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering.The MIT Press,1996,ISBN 0-262-11212-4 (EN)
Schalkoff,R.J.:Artificial Neural Networks. The MIT Press,1997,ISBN 0-07-115554-6 (EN)
Hlaváč V.- Šonka M.: Počítačové vidění. Grada 1992,Praha,ISBN 80-85424-67-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 1. ACADEMIA 1993,Praha,ISBN 80-200-0496-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 2. ACADEMIA 1997,Praha,ISBN 80-200-0504-8 (CS)
Berka P. a kol.: Expertní systémy. Skripta, VŠE Praha, 1998. (CS)
Šíma J., Neruda R.: Teoretické otázky neuronových sítí. Matfyzpress, Praha 1996 (CS)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson, 2008, ISBN 978-0-495-08252-1 (EN)
Planned learning activities and teaching methods
Techning methods include lectures and computer laboratories. Students have to write a single project during the course.
Assesment methods and criteria linked to learning outcomes
Condition of petting full credit is absolut (100%) attendance in obligatory parts of lessons - the computer exercises. Students are tested continuously and i tis possible to get maximum 20 points. The final written exam is rated by 70 points at maximum and the oral exam is rated by 10 points at maximum.
Language of instruction
1. Modern means in automation - relevance, resources, engineering cybernetics.
2. Data, information, knowledge - definition, examples.
3. Expert systems - definitiv, architectural engeneering, theoretical sources, characteristics, inference engine, creation of knowledge base, acguirement of knowledges, proces sof consultation, aplications.
4. Artificial neural networks - definition, neuron, topology, paradigm, multilayer neural network, backpropagation algorithm, activation, characteristics.
5. Theory of Inventive Problem Solving - analysis of the object to be improved and formulation of innovative task to be solved, then solving of inventive tasks supported by expert system and information from world patent databases.
6. Computer vision - preprocessing, image segmentation, description, classification.
The aim of the course is to acquaint the students with modern methods and means in automation. To get knowledge and experience of pattern recognition and using neural networks, expert systems in automation.
Specification of controlled education, way of implementation and compensation for absences
The computer exercises are compulsory, the properly excused missed computer exercises can be compensate.
Type of course unit
26 hours, optionally
Teacher / Lecturer
Exercise in computer lab
39 hours, compulsory
Teacher / Lecturer
eLearning: currently opened course