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Unit information: Intelligent Adaptive Systems (UWE) in 2020/21

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 (UFME7K-15-M) 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/ (UWE login required) for full details.

Intended learning outcomes

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

  • Demonstrate a thorough understanding of the important features of intelligent and adaptive systems using both basic and compound architectures [Assessed in component A]
  • Critically compare the performance 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 and B]
  • Apply the principles covered in this module in a practical robotics application. [Assessed in component B]
  • Study independently where necessary for the understanding of new advancements in the field [Assessed in components A and B]
  • Transfer these advanced new techniques from the research sector to industrially-relevant applications [Assessed in components A and B]

Teaching details

Lectures will introduce the fundamental concepts. Tutorial sessions will be used for two purposes: they will be used to expose students to demonstrations of the basic architectures in action as well as to discuss real implementations of these new techniques. Tutorials are 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.

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

The following list is offered to provide an indication of the type and level of information students may be expected to consult. As such, its currency may wane during the life span of the module specification. However, CURRENT advice on readings will be available via other more frequently updated mechanisms.


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 (1991) Neural Computing - an introduction. Adam Hilger.


Raúl Rojas (1991). Neural Networks. A Systematic Introduction. Springer. Berlin


Brown & Harris (1994) Neurofuzzy Adaptive Modelling and Control. Prentice Hall. [ISBN: 0131344536]


A.E. Eiben and J.E. Smith (2003), Introduction to Evolutionary Computing, Springer


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 Fuzzy, Neural Network, and Simulink Toolboxes.

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