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Unit information: Intelligent Adaptive Systems (UWE) in 2016/17

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Unit name Intelligent Adaptive Systems (UWE)
Unit code EMATM0034
Credit points 15
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Dogramadzi
Open unit status Not open
Pre-requisites

None

Co-requisites

None

School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

Description

This unit will be delivered at the University of the West of England (UWE)

This module focuses on intelligent control techniques. See https://secure.uwe.ac.uk/fet/uwedocs/modules/EDM/M/UFMFPC-30-M_1.pdf (UWE login required) for full details.

Intended learning outcomes

On successful completion of this module students will be able to:

1. Show a knowledge and understanding of the critical features of intelligent and adaptive systems. (Assessed in components A, B)

2. Show a knowledge and understanding of appropriate terminology and working definitions in the subject. (Assessed in components A, B)

3. Compare the characteristics of the advanced new techniques covered in this module with traditional approaches to selected problems in signal processing, classification and control. (Assessed in components A, B)

4. Demonstrate communication skills. (Assessed in component B)

5. Demonstrate IT skills in context (Assessed in component B)

6. Demonstrate ability to formulate problems, critically analyse them and evaluate appropriate techniques for their solution (Assessed in component A,B)

Teaching details

Lectures will introduce the fundamental concepts. Tutorial case study sessions will be used for two purposes. They will be used to expose students to demonstrations of the basic architectures in action. They will also be used to discuss real implementations of these new techniques, each designed to illustrate the essential details of a particular concept or technique, and especially its strengths and weaknesses in both technical and business contexts. At all times specific examples will be used to "ground" the theory and students will use the case study material to contribute towards the coursework assignment.

Assessment Details

End of module examination (50%, “Component A”) to assess individual abilities on problem analysis and subject knowledge. One coursework assignment (50%, “Component B”) that assesses practical design and implementation abilities and understanding of a chosen topic from the syllabus.

Reading and References

  • Nie & Linkens (1995). Fuzzy-Neural Control: Principles, Algorithms and Applications, Prentice Hall (ISBN:0133379167)
  • White & Sofge (1992). The Handbook of Intelligent Control, Van Nostrand-Reinhold
  • Beal, R & Jackson, T (1990). Neural Computing - an introduction, Adam Hilger
  • Rao & Rao (1995). C++ Neural network and Fuzzy Logic, 2nd Edition, MIS (ISBN:15585515526)
  • Brown & Harris (1994). Neurofuzzy Adaptive Modelling and Control, Prentice Hall (ISBN:0131344536)
  • Miller, Sutton & Werbos (1991). Neural Networks for Control, MIT Press
  • Arbib, M.A (1995). The Handbook of Brain Theory and Neural Networks, MIT Press

Design tool user manuals, e.g. MATLAB 6.2 Fuzzy, Neural Network, and Simulink Toolboxes

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