Incremental network construction algorithm for approximating discontinuous functions

Hyukjoon Lee, Kishan Mehrotra, Chilukuri K Mohan, Sanjay Ranka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Traditional neural network training techniques do not work well on problems with many discontinuities, such as those that arise in multicomputer communication cost modeling. We develop a new algorithm to solve this problem. This algorithm incrementally adds modules to the network, successively expanding the 'window' in the data space where the current module works well. The need for a new module is automatically recognized by the system. This algorithm performs very well on problems with many discontinuities, and requires fewer computations than traditional backpropagation.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE Computer Society
Pages2191-2196
Number of pages6
Volume4
StatePublished - 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

ASJC Scopus subject areas

  • Software

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    Lee, H., Mehrotra, K., Mohan, C. K., & Ranka, S. (1994). Incremental network construction algorithm for approximating discontinuous functions. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 4, pp. 2191-2196). IEEE Computer Society.