Unit information: Intelligent & Adaptive Systems (UWE) in 2012/13

Unit name	Intelligent & Adaptive Systems (UWE)
Unit code	MENGM0009
Credit points	15
Level of study	M/7
Teaching block(s)	Teaching Block 1 (weeks 1 - 12)
Unit director
Open unit status	Not open
Pre-requisites	None
Co-requisites	None
School/department	Department of Mechanical Engineering
Faculty	Faculty of Engineering

Description including Unit Aims

This unit is provided by UWE.

Introduction: Review of the links with other disciplines, e.g. classical AI, psychology, robotics, ethology, neuroscience and classical control. Scope and limitations of this module, especially with respect to classical control and AI. Learning and adaptive systems: Working definitions of intelligence, adaptive systems and learning. Adaptation through learning versus design. Basic Architectures: Neural networks. Fuzzy systems. Evolutionary computation. Supervised, unsupervised and reinforcement learning. Compound Architectures: Classifier Systems. Behaviour-based systems. Agent-based systems. Multi-agent systems. Example applications: Review of work carried out in this Faculty, and at other establishments, in order to demonstrate the major strengths and weaknesses of the techniques. For example; intelligent multiple agents for fault diagnosis in electrical power distribution systems, fuzzy control of an automated underground transportation system, co-operative behaviour in multi-agent mobile robotics, neurocontrol of an industrial robot manipulator, fuzzy classifier systems for telecommunications network routing, evolutionary computation as an aid to engineering design, human face and handwriting recognition using neural networks.

Intended Learning Outcomes

On completion of this module a student will typically be able to:- Show a detailed knowledge and understanding of

• A thorough understanding of the critical features of intelligent and adaptive systems, basic and compound architectures
• An understanding of the appropriate terminology and working definitions

Demonstrate subject specific skills with respect to

• He student will be bale to 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.
• The student will have been exposed to examples of these advanced new techniques being transferred from the research sector to industry, based upon experiences in this and other Universities

Show cognitive skills with respect to

• Apply the principles covered in this module elsewhere
• Study independently where necessary for the understanding of new advancements in the field

Demonstrate key transferable skills in

• Communication skills
• self-management skills
• IT skills in context
• problem formulation and decision making
• progression to independent learning

Teaching Information

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.

Assessment Information

Assessment Weighting between components A and B A: 50% B: 50%

ATTEMPT 1 First Assessment Opportunity Element Description Element Type % of Component % of Assessment Component A (Controlled Conditions) Examination (180 mins) Exam 100% 50% Component B Assignment 1 Coursework 50% 25% Assignment 2 Coursework 50% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Element Description Element Type % of Component % of Assessment Component A (Controlled Conditions) Examination (180 mins) Exam 100% 50% Component B Written Assignment Coursework 100% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is not required.

Reading and References

Indicative Reading List The following list is offered to provide validation panels/accrediting bodies with 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. Internet sources: There is a very wide range of up-to-date information on these subjects, at the appropriate introductory level for this module, available on the internet. Students will be provided with a Compact Disk that guides their access to approved and publicly available sites (such as the Evonet “Flying Circus” site for evolutionary computation). There are also some older books that still represent excellent introductions to this subject area.

• 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 Fizzy 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 Teory and Neural Networks, MIT Press

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