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On the design and initialization of layered feed-forward neural networks

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2 Author(s)
Maccato, A. ; Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA ; de Figueiredo, R.J.P.

This paper considers the design and initialization of a network, based on application specific knowledge, available at design time. We describe a methodology for translating high level knowledge about an application into a neural network interconnection specification. The program's design philosophy stresses separation of neural design from network function, a uniform syntax for neurons, inputs, and outputs, and flexibility in modularizing the resulting network. The ability to train neural networks allows the encoded knowledge to be further fine tuned for a specific data space. Moreover, the translation rules can allow for the selective training of subnetworks

Published in:

Neural Networks, 1995. Proceedings., IEEE International Conference on  (Volume:2 )

Date of Conference:

Nov/Dec 1995