Abstract
A novel connectionist neural network model based on a propagation rule which contains Multilayer Perceptron (MLP) and Radial Basis Function (RBF) parts is introduced in this paper. The network using this propagation rule is known as a Conic Section Function Network (CSFN). Two different strategies have been used for training the network. The contact lens fitting problem has been considered to demonstrate the performance of the training algorithms. The performances of a standard MLP trained by back propagation, a fast back propagation with adapted learning rates, a standard RBFN using Matlab Neural Network software toolbox, and the proposed algorithm are compared for this particular problem.
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© 1998 Springer-Verlag London Limited
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Yildirim, T., Marsland, J.S. (1998). A unified framework for connectionist models. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_3
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DOI: https://doi.org/10.1007/978-1-4471-1546-5_3
Publisher Name: Springer, London
Print ISBN: 978-3-540-76208-9
Online ISBN: 978-1-4471-1546-5
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