Recurrent back-propagation and Newton algorithms for training recurrent neural networks

Chung-Ming Kuan; Kurt Hornik; Tung Liu

doi:10.1117/12.172502

1 February 1994 Recurrent back-propagation and Newton algorithms for training recurrent neural networks

Chung-Ming Kuan, Kurt Hornik, Tung Liu

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Proceedings Volume 2093, Substance Identification Analytics; (1994) https://doi.org/10.1117/12.172502
Event: Substance Identification Technologies, 1993, Innsbruck, Austria

Abstract

In this paper the recurrent back-propagation and Newton algorithms for an important class of recurrent networks and their convergence properties are discussed. To ensure proper convergence behavior, recurrent connections must be suitably constrained during the learning process. Simulation results demonstrate that the algorithms with the suggested constraint have superior performance.

Citation Download Citation

Chung-Ming Kuan, Kurt Hornik, and Tung Liu "Recurrent back-propagation and Newton algorithms for training recurrent neural networks", Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); https://doi.org/10.1117/12.172502

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