Registry

Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title Intelligent Systems
Module Code EE456
School School of Electronic Engineering
Online Module Resources

NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Description
The purpose of this module is to enable students to develop a knowledge of the theory of intelligent systems for advanced control and system identification. Students will attain skills in using MATLAB and SIMULINK software tools to design, simulate and evaluate intelligent systems solutions. This module is delivered in both on-campus and distance learning modes and students can avail of either or both modes to suit their own learning needs. Students are expected to engage in lectures and tutorials in either or both modes.

Learning Outcomes
1. Present and derive a range of algorithms relevant to the field of intelligent systems
2. Design a range of solutions to real-world identification and control problems using appropriately selected intelligent systems approaches
3. Implement a range of intelligent system solutions using MATLAB and SIMULINK software tools
4. Simulate intelligent systems for control and system identification using MATLAB and/or SIMULINK and display the results of these simulation in graphical formats
5. Evaluate and critically compare the effectiveness of intelligent systems solutions by measuring appropriate performance indicators
6. Effectively discuss the design, simulation and evalutation process and results by written means



Workload Full-time hours per semester
Type Hours Description
Lecture36Formal lecture (recorded and made available via web)
Tutorial10Computer-based problem solving
Examination3End-of-Semester computer-based exam
Assignment30Assignment 1 - Computer-based problem solving
Assignment20Assignment 2 - Computer-based design
Independent learning30Online activity with module material
Independent learning58.5General revision and practice of computer-based content
Total Workload: 187.5

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities
MATLAB & SIMULINK.
Introduction to MATLAB & SIMULINK and the use of appropriate software toolboxes to address the design and analysis needs of the module..

System Identification.
How is system identification approached for linear systems? What kinds of linear models are appropriate. Least squares is examined with a view to developing the recursive least squares approach. What are the issues involved in setting up a system identification solution?.

Self-Tuning Controller.
Once the system identification has been achieved, how can this be used as part of a self-tuning controller? How is pole placement used as an approach to adaptive controller design and what are the potential issues associated with this approach?.

Fuzzy Logic Systems.
What are fuzzy sets and fuzzy logic systems. How is this approach used in control system design? How can fuzzy controllers be improved?.

Genetic Algorithms.
What are genetic algorithms and how do the component parts of the algorithm operate? How can these algorithms be applied to control and system identification problems and how do the algorithm parameters influence the success of the algorithm? How can design problems be represented in objective functions?.

Artificial Neural Networks.
What are Artificial Neural Networks (ANNs)? How does a perceptron work and how can perceptron learning be applied to logical operations and classification problems? How can the single-layer perceptron principles be extended to multi-layer perctpetrons? How can SLPs and MLPs be used in system identification and/or control system design?.

Learning Activities.
Students learn through computer-based, problem-led examples presented in lectures, tutorials and via web delivery. The continuous assessment involves two individualized problem-based assignments that students carry out independently..

Assessment Breakdown
Continuous Assessment25% Examination Weight75%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentSystem Identification Assignment15%Week 25
AssignmentIntelligent System Design Assignment10%Sem 2 End
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List
  • Fakhreddine O. Karray and Clarence de Silva: 2004, Soft computing and intelligent systems design : theory, tools and applications, Addison-Wesley, Harlow, 0321116178
  • Gene F. Franklin, J. David Powell, Michael L. Workman: 1998, Digital control of dynamic systems, 3rd Ed., Chapters 2,3,4 & 8, Addison-Wesley, Menlo Park, Calif., 0201820544
  • P.E. Wellstead and M.B. Zarrop: 1991, Self-tuning systems : control and signal processing, Wiley, Chichester ; New York, 0471928836
  • Kevin M. Passino, Stephen Yurkovich: 1998, Fuzzy control, http://www.ece.osu.edu/~passino/FCbook.pdf, Addison-Wesley, Menlo Park, Calif, 020118074X
  • Simon Haykin: 1999, Neural networks : a comprehensive foundation, 2nd Ed., Prentice Hall, Upper Saddle River, NJ, 0132733501
Other Resources
760, Website, Jennifer Bruton, 0, EE456 Notes, www.eeng.dcu.ie/~ee456,
Array
Programme or List of Programmes
MTCMEng in Telecommunications Engineering
Timetable this semester: Timetable for EE456
Date of Last Revision03-FEB-10
Archives:
DCU
Address
The Helix
3U
NDP
Government
EU
Disclaimer | Privacy