SCHOOL

School of Engineering

ACADEMIC UNIT

Department of electrical and Computer Enginering

LEVEL OF STUDIES

Postgraduate

COURSE CODE

ECE_ΔK807

SEMESTER

8^th

COURSE TITLE

INTELLIGENT CONTROL

INDEPENDENT TEACHING ACTIVITIES
if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

WEEKLY TEACHING HOURS

CREDITS

3

5

Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

COURSE TYPE

general background,
special background, specialised general knowledge, skills development

Specialised general knowledge, skills development

PREREQUISITE COURSES:

Linear Control Systems, Applied Optimization, Artificial Intelligence

LANGUAGE OF INSTRUCTION and EXAMINATIONS:

Greek

IS THE COURSE OFFERED TO ERASMUS STUDENTS

NO

COURSE WEBSITE (URL)

To be created at e-class after the lesson is validated

Learning outcomes

The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

Consult Appendix A

·     Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area

·     Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B

·     Guidelines for writing Learning Outcomes

Upon successful conclusion of this course, the students should be able to:

1.       Understand fundamental concepts of neural networks and their application in control systems.

2.       Understand fundamental concepts of fuzzy systems and their application in control systems.

3.       Design basic reinforcement learning control schemes.

4.       Apply evolutionary algorithms to optimize the response of control systems.

5.       Simulate intelligent control systems in various application domains.

General Competences

Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Adapting to new situations

Decision-making

Working independently

Team work

Working in an international environment

Working in an interdisciplinary environment

Production of new research ideas

Project planning and management

Respect for difference and multiculturalism

Respect for the natural environment

Showing social, professional and ethical responsibility and sensitivity to gender issues

Criticism and self-criticism

Production of free, creative and inductive thinking

……

Others…

…….

Decision-making

Working independently

Team work

Working in an interdisciplinary environment

Project planning and management

Introduction to classic and intelligent control systems. Intelligent control systems and artificial intelligence. Artificial neural networks: introduction, structures, training, applications. Introduction to fuzzy logic. Fuzzy control systems: introduction, theory, applications. Neuro-fuzzy control: theory, design, applications. Reinforcement learning control. Stochastic and evolutionary algorithms. Optimization of intelligent control systems based on genetic algorithms.

DELIVERY
Face-to-face, Distance learning, etc.

Lectures

USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
Use of ICT in teaching, laboratory education, communication with students

YES

TEACHING METHODS

The manner and methods of teaching are described in detail.

Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

STUDENT PERFORMANCE EVALUATION

Description of the evaluation procedure

Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

Homeworks during the semester that include technical reports and simulations

Study and presentation of a scientific paper related to the topic of the course

Final exams

- Suggested bibliography:

1) «Intelligent Systems - Modeling, Optimization, and Control», Y. C. Shin, C. Xu, CRC Press, 2009.