Cybernetics and Neural Networks (100H6)
Cybernetics and Neural Networks
Module 100H6
Module details for 2022/23.
15 credits
FHEQ Level 7 (Masters)
Module Outline
A cybernetic device responds and adapts to a changing environment in a sensible way. Neural network systems permit the construction of such devices exploiting information, feedback and control to achieve intelligent interaction and behaviour from autonomous devices such as robots. In this module the utilisation of artificial intelligence techniques and neural networks are explored in detail. Software implementation of theoretical concepts will solve genuine engineering problems in dynamic feedback control systems, pattern recognition and scheduling problems. In many instances solutions must be computed in response to data arriving in real-time (e.g. video data). The implications of high speed decision making will be explored.
The module will explore:
Neuron Models, Network Architectures, Perceptron and Perceptron learning rule, Synaptic Vector Spaces, Linear transformations for Neural Networks, Supervised Hebbian Learning, Performance Optimisation, Widrow-Hoff Learning, Associative learning, Competitive Networks.
Learning will be supported by laboratories using the Matlab Neural Network Toolbox.
AHEP4 Learning Outcomes
M1, M2, M3, M4, M5, M8, M12, M17
Library
Martin T. Hagan, "Neural Network Design", PWS Publishing Company, ISBN 0-534-94332-2, 1996, QA76.87.H34
Alison Cawsey, "The Essence of Artificial Intelligence", Prentice Hall, ISBN 0-13-571779-5, 1998, QZ1250 Caw
S. Haykin "Neural Networks: A comprehesive Foundation", MacMillan, ISBN 0-13-273350-1, 1999, QZ 1335 Hay
Howard L. Resnikoff "The Illusion of Reality", Springer-Verlag, ISBN 0-387-96398-7, 1989, QE 1300 Res
A. White, A Sofge "Handbook of Intelligent Control: Neural, Fuzzy and Adaptive Approaches" Van Nostrand Reihold, 1992, QZ 1275 Han
Module learning outcomes
The fundamental principles of neural network systems and their applications.
A range of specialist topics related to neural network systems.
Current problems and emerging solutions in the applications of neural networks.
The analytical and practical techniques applicable to advanced scholarship in neural networks systems.
| Type | Timing | Weighting |
|---|---|---|
| Computer Based Exam | Semester 1 Assessment | 80.00% |
| Coursework | 20.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | T1 Week 11 | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Laboratory | 1 hour | 00111111000 |
| Autumn Semester | Lecture | 2 hours | 01111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Prof Chris Chatwin
Assess convenor
/profiles/9815
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