Study of Improved BP Algorithm based on Gradient Descent and Numerical Optimization

Qiuhong Sun, Weihong Bi, Xinhang Xu

Abstract

Studied limitations exist in BP model, and discussed the proposed improved algorithm based on BP neural network. Respectively, from the third of the aspects discussed based on improved gradient descent algorithm and improved algorithm based on numerical optimization. Research results showed that the comprehensive method is better than the standard BP algorithm in terms of the number of iterations, the training time and the mean square error and the like, of additional momentum and adaptive outstanding performance parameter method. Researches showed that the Marquardt-Levenberg algorithm neural network convergence fastest training times at least.

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Paper Citation


in Harvard Style

Sun Q., Bi W. and Xu X. (2015). Study of Improved BP Algorithm based on Gradient Descent and Numerical Optimization . In Proceedings of the Information Science and Management Engineering III - Volume 1: ISME, ISBN 978-989-758-163-2, pages 452-456. DOI: 10.5220/0006028404520456


in Bibtex Style

@conference{isme15,
author={Qiuhong Sun and Weihong Bi and Xinhang Xu},
title={Study of Improved BP Algorithm based on Gradient Descent and Numerical Optimization},
booktitle={Proceedings of the Information Science and Management Engineering III - Volume 1: ISME,},
year={2015},
pages={452-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006028404520456},
isbn={978-989-758-163-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Information Science and Management Engineering III - Volume 1: ISME,
TI - Study of Improved BP Algorithm based on Gradient Descent and Numerical Optimization
SN - 978-989-758-163-2
AU - Sun Q.
AU - Bi W.
AU - Xu X.
PY - 2015
SP - 452
EP - 456
DO - 10.5220/0006028404520456