CSE5301 Neuro-Fuzzy Computing
A/Prof. Andrew P. Paplinski
Prerequisite knowledge
Basic knowledge of vectors and matrices is assumed. Specialised mathematical
concepts will be introduced.
Syllabus
This units examines mathematical and computational fundamentals of
artificial neural networks and Fuzzy systems, and their applications in signal
and image processing, pattern recognition and modelling. The syllabus includes:
- Basic concepts of neurocomputing:
- Artificial Neural Networks (ANN) and their biological roots and
motivations. ANNs as numerical data/signal/image processing devices.
Encoding (training phase) and decoding (active phase). Taxonomy of
neural networks: feedforward and recurrent networks with supervised and
unsupervised learning laws. Static and dynamic processing systems.
Basic data structures: mapping of vector spaces, clusters, principal
components.
- Basic terminology related to an artificial
neuron:
- a summing dendrite, synapses and their weights, pre- and
post-synaptic signals, activation potential and activation function.
Excitatory and inhibitory synapses. The biasing input. Types of
activating functions.
- The Perceptron
- The Perceptron and its learning law. Classification of linearly
separable patterns.
- Linear Networks.
- Adaline --- the adaptive linear element. Linear regression. The
Wiener-Hopf equation. The Least-Mean-Square (Widrow-Hoff) learning
algorithm. Method of steepest descent. Adaline as a linear adaptive
filter. A sequential regression algorithm.
- Multi-Layer Feedforward Neural Networks:
- aka Multi-Layer Perceptrons. Supervised Learning. Approximation and
interpolation of functions. Radial-Basis functions.
Back-Propagation Learning law. Fast
training algorithms. Applications of multilayer perceptrons: Image
coding, Paint-quality inspection, Nettalk.
- Self-Organising systems.
- Unsupervised Learning. Local learning laws. Generalised Hebbian
Algorithm. The Oja's and Sanger's rules. Principal component
analysis --- Karhunen-Loeve transform.
- Competitive Learning:
- MinNet and MaxNet networks. Clustering. Learning Vector
Quantisation. Codebooks. Application in data compression.
- Self-Organising Feature Maps:
- Kohonen networks.
- Recurrent networks
- Hopfield networks.
- Fuzzy logic Systems
- Basic definitions and operations.
- Fuzzy relations
- Fuzzy rules
- Fuzzy inference
- Fuzzification and de-fuzzification
- Adaptive Neuro-Fuzzy Inference Systems
Recommended references:
- Simon Haykin, Neural Networks -- a Comprehensive Foundation,
Prentice Hall, 2nd ed., 1999, ISBN 0-13-273350-1
- Andrew P. Paplinski,
CSE5301 Lecture notes
- H. Demuth, M. Beale,
Neural Network Toolbox. For use with MATLAB. User's Guide
The MathWorks Inc, (Huge file!)
-
Fuzzy LogicToolbox. For use with MATLAB. User's Guide
The MathWorks Inc, (Huge file!)
- Martin T. Hagan,. H. Demuth,M. Beale, Neural Network Design,
PWS Publishing, 1996, ISBN 0-534-94332-2
- A. Konar,
Computational Intelligence Principles, Techniques and Applications.
Springer, 2005, ISBN: 3-540-20898-4
- W. S. Sarle, editor.
Neural Nets FAQ
Subject structure and organisation
The subject format is based on two hours a week of lectures and two hours a
week of practical/tutorial work. It is expected that in addition a student spends
approximately 6 hours a week on theoretical and practical aspects related to
the unit.
Practical work
Practical work related to the unit is based on the MATLAB package.
MATLAB is available on many (but not all) Unix/Linux/Windows platforms around
the campus.
You can also purchase a MATLAB Student Version, in
particular, from
http://www.mathworks.com/academia/student_version/
Introduction to MATLAB is given in Practical 1.
Make sure that you have your computer account active asap.
Assessment
is based on assignments and practical works (50%) and two-hour exam (50%).
Plagiarism
Students should consult University materials on cheating, in
particular:
- Student Resource Guide - section on Student Rights and
Responsibilities at
http://www.monash.edu.au/pubs/handbooks/srg/srg0059.htm
- Student Resource Guide at
http://www.monash.edu.au/pubs/handbooks/srg/, particularly the
section on Cheating at
http://www.monash.edu.au/pubs/handbooks/srg/srg0071.htm
It is the student's responsibility to make themselves familiar
with the contents of these documents.
Andrew P. Paplinski
25 February 2005