New globally convergent training scheme based on the resilient propagation algorithm

Anastasiadis, A.D. and Magoulas, George and Vrahatis, M.N. (2005) New globally convergent training scheme based on the resilient propagation algorithm. Neurocomputing 64 , pp. 253-270. ISSN 0925-2312.

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Official URL: https://doi.org/10.1016/j.neucom.2004.11.016

Abstract

In this paper, a new globally convergent modification of the Resilient Propagation-Rprop algorithm is presented. This new addition to the Rprop family of methods builds on a mathematical framework for the convergence analysis that ensures that the adaptive local learning rates of the Rprop's schedule generate a descent search direction at each iteration. Simulation results in six problems of the PROBEN1 benchmark collection show that the globally convergent modification of the Rprop algorithm exhibits improved learning speed, and compares favorably against the original Rprop and the Improved Rprop, a recently proposed Rrpop modification.

Metadata

Item Type:	Article
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	22 Jun 2021 12:46
Last Modified:	09 Aug 2023 12:51
URI:	https://eprints.bbk.ac.uk/id/eprint/44840

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