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Multiple Shape Basis Function Networks for Rule Based Analysis of Data.

Halgamuge, S.K.

In: Proc. Australian New Zealand Conference on Intelligent Information Systems (ANZISS'96), Adelaide, South Australia, Nov. 18-20, 1996, pages 113-116. IEEE, 1996.

Abstract: The concept of Radial Basis Functional Networks has been generalised introducing Multiple Shape Basis Function. The learning algorithm Restricted Coulomb Energy Learning, capable of generating the hidden layer dynamically, is extended including new components to adjust the shape of the region of attraction of a prototype neuron in addition to the adaptation of centre weight and radial parameters so that the input space is covered more efficiently by automatically generating clusters of different sizes and shape. This shows a clear reduction in number of neurons or the number of fuzzy rules generated and the classification accuracy is increased significantly. This improvement is highly relevant in developing neural networks functionally equivalent to fuzzy classifiers since the transparency is strongly related to the compactness of the generated system.

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