Fast learning artificial neural networks for classification

• Main Author: Tay, Leng Phuan
• Published: Loughborough University 1994
• Subjects: 003.5; Bionics
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307406

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 problems.