Acceleration of back propagations through initial weight pre-training with delta rule

Gang Li, Hussein Alnuweiri, Yuejian Wu, Hongbing Li

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

22 Citations (Scopus)

Abstract

A new training strategy for Back Propagation (BP) neural networks, named Delta Pre-Training (DPT), is proposed. The core of the new training strategy is based on pre-training the initial weights for BP networks using the Delta rule, instead of using random values. After pre-training, the normal BP training procedure is carried out to complete network training. With the DPT, the convergence rate for training BP networks can be significantly improved. Since the DPT deals only with initial weight settings, most variations of the standard BP algorithm (aiming at increasing convergence rate) can be combined with the DPT so as to further speed up convergence. With regards to on-chip learning in VLSI implementations, only a little additional circuitry is required for the pre-training phase with the DPT. Simulation results using the proposed training method show its superiority over previous methods.

Original languageEnglish
Title of host publication1993 IEEE International Conference on Neural Networks, ICNN 1993
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages580-585
Number of pages6
Volume1993-January
ISBN (Electronic)0780309995
DOIs
Publication statusPublished - 1 Jan 1993
Externally publishedYes
EventIEEE International Conference on Neural Networks, ICNN 1993 - San Francisco, United States
Duration: 28 Mar 19931 Apr 1993

Other

OtherIEEE International Conference on Neural Networks, ICNN 1993
CountryUnited States
CitySan Francisco
Period28/3/931/4/93

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Artificial Intelligence

Cite this

Li, G., Alnuweiri, H., Wu, Y., & Li, H. (1993). Acceleration of back propagations through initial weight pre-training with delta rule. In 1993 IEEE International Conference on Neural Networks, ICNN 1993 (Vol. 1993-January, pp. 580-585). [298622] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNN.1993.298622