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|Title: ||Fast learning neural networks for classification|
|Authors: ||Tay, Leng Phuan|
|Issue Date: ||1994|
|Publisher: ||© Tay Leng Phuan|
|Abstract: ||Neural network applications can generally be divided into two categories. The first
involves function approximation, where the neural network is trained to perform intelligent
interpolation and curve fitting from the training data. The second category involves
classification, where specific exemplar classes are used to train the neural network. This
thesis directs its investigations towards the latter, i.e. classification.
Most existing neural network models are developments that arise directly from human
cognition research. It is felt that while neural network research should head towards the
development of models that resemble the cognitive system of the brain, researchers should
not abandon the search for useful task oriented neural networks. These may not possess the
intricacies of human cognition, but are efficient in solving industrial classification tasks.
It is the objective of this thesis to develop a neural network that is fast learning, able
to generalise and achieve good capacity to discern different patterns even though some
patterns may be similar in structure. This eventual neural network will be used in the
pattern classification environment.
The first model developed, was the result of studying and modifying the basic ART I
model. The "Fast Learning Artificial Neural Network I" (FLANN I) maintains good
generalisation properties and is progressive in learning. Although this neural network
achieves fast learning speeds of one epoch, it was limited only to binary inputs and was
unable to operate on continuous values. This posed a real problem because industrial
applications usually require the manipulation of continuous values.
The second model, FLANN II, was designed based on the principles of FLANN I. It was
built on the nearest neighbour recall principle, which allowed the network to operate On
continuous values. Experiments were conducted on the two models designed and the results
were favourable. FLANN II was able to learn the points in a single epoch and obtain
exceptional accuracy. This is a significant improvement to other researcher's results.
A further study was conducted on the FLANN models in the parallel processing
environment. The parallel investigations led to the development of a new paradigm;
Parallel Distributed Neural Networks (PDNNs), which allows several neural networks to
operate concurrently to solve a single classification problem. This paradigm is powerful
because it is able to reduce the overall memory requirements for some classification
|Description: ||A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.|
|Appears in Collections:||PhD Theses (Computer Science)|
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