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CIRG - Research  -  Neural Networks 



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OVERVIEW

The focus of the neural networks group is to investigate aspects of training and optimization of neural networks, and to apply neural networks to solve real-world problems. The activities of this focus area are mainly centered around architecture selection, active learning, and the development of new an efficient training algorithms. Some work is done on self-organizing maps.

Current applications are directed towards data mining, spam detection, user authentication, fraud detection, gesture recognition, and trading on financial markets. Applications of self-organization maps to exploratory data analysis, data mining, and species identification are done.

ACTIVE MEMBERS

List the current members actively doing research in this focus area. [ Show ]

ALUMNI MEMBERS

S van der Stockt

M.Sc Completed in 2008

E Dean

M.Sc Started in 2002

J Pun

M.Sc Completed

E Clements

Hons-B.Sc Completed in 2003

U Paquet

M.Sc Completed in 2003

R van den Hoven

Hons-B.Sc Completed in 2003

A Ismail

PhD Started in 2005
M.Sc Completed in 2001

A Adejumo

M.Sc Completed in 1999

GROUP PUBLICATIONS

List publications of this research focus area. [ Show ]

MEMBER PROFILE



 Name:

 Adiel Ismail

Portrait photo

 E-mail:

 aismail@uwc.ac.za

 Group(s):

 Swarm Intelligence
Neural Networks

 

 Degree specific information: PhD

 Title:

 Non-Parametic PSO

 Abstract:

Not available

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

 Not available for download yet.

 

 Degree specific information: M.Sc

 Title:

 Training and Optimization of Product Unit Neural Networks

 Abstract:

Product units in the hidden layer of multilayer neural networks provide a pwerful mechanism for neural networks to efficiently learn higher-order combinations of inputs. Training product unit neural networks using local optimization algorithms is difficult due to an increased number of local minima and increased chances of network paralysis. This research investigates the problems using local optimization, especially gradient descent, to train product unit neural networks, and shows that particle swarm optimization, genetic algorithms and leapfrog are efficient alternatives to successfully train product unit neural networks. Architecture selection, i.e. pruning, of product unit neural networks is also studied and a pruning algorithm developed.

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

 Download




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