[1]赵启林,卓家寿,柳景春.BP网络的BP-G-S学习算法[J].南京理工大学学报(自然科学版),1999,(04):308-311.
 ZhaoQilin ZhuoJiashou LiuJingchuen.The BP G S Learning Algorithms of BP Network[J].Journal of Nanjing University of Science and Technology,1999,(04):308-311.
点击复制

BP网络的BP-G-S学习算法()
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
1999年04期
页码:
308-311
栏目:
出版日期:
1999-08-30

文章信息/Info

Title:
The BP G S Learning Algorithms of BP Network
作者:
赵启林卓家寿柳景春1
河海大学土木学院, 南京210098
 ¹ 工程兵工程学院人防工程系, 南京210007
Author(s):
ZhaoQilin ZhuoJiashou LiuJingchuen ①
School of Civil Engineering of Ho Hai University,Nanjing 210094
¹ Department of Civil Engineering of Nanjing Eng ineering Institute, Nanjing 210007
关键词:
神经网络 迭代法 快速 算法
Keywords:
neural netw orks iterat ion methods rapid algorithms
分类号:
TP18
摘要:
为了解决BP网络在学习过程中存在收敛慢的缺点,文中将GaussSeidel 迭代法的基本思想与BP算法结合,将每一个最新修正的权值反映到下一个权值的修正中,提出了一种新的BPGS学习算法来加速BP网络的收敛。文章最后的计算机仿真说明BPGS总体上可以减少学习的时间,尤其当误差值逼近最小点时效果明显。
Abstract:
To solve the problem of the slowness of converg ence in the learning process of BP network, this paper integrates the idea of Gauss-Seidel algorithms into BP algorithms and makes use of the new ly corrected w eight w hile correct ing next weight . The imitat ion of computer proves that BP-G-S algorithm may decrease learning t ime in the w hole. The effect is obvious especially w hen the error value is near to the opt imal point .

参考文献/References:

1 赵振宇, 徐用懋.模糊理论和神经网络的基础与应用.北京: 清华大学出版社, 1996. 104~107
2 邓建中, 葛仁杰, 程正兴.计算方法. 西安: 西安交通大学出版社, 1985.201~ 204
3 邓志刚, 孙增圻1BP 网络的PID 型二阶快速学习算法. 自动化学报, 1995, 21( 4) : 19~ 25
4 Billing S A, Chen S, Koreberg M J. Identification of MIMO nonlinear using a forward regression orthogonal estimators. I nternational Journal of control, 1989, 49: 2157~ 2189
5 焦李成.神经网络计算.西安: 西安电子科技大学出版社, 1993.196~ 217

相似文献/References:

[1]何春梅,叶征春,韩牟,等.形态双向联想记忆网络的摄动鲁棒性研究[J].南京理工大学学报(自然科学版),2011,(05):664.
 HE Chun-mei,YE Zheng-chun,HAN Mu,et al.Perturbation Robustness of Morphological Bidirectional Associative Memories Networks[J].Journal of Nanjing University of Science and Technology,2011,(04):664.
[2]刘永建,朱剑英,曾捷,等.改进BP神经网络在发动机性能趋势分析和故障诊断中的应用[J].南京理工大学学报(自然科学版),2010,(01):24.
 LIU Yong-jian,ZHU Jian-ying,ZENG Jie.Improved BP Neural Network System for Engine Performance Trend Analysis and Fault Diagnosis[J].Journal of Nanjing University of Science and Technology,2010,(04):24.
[3]黄俊,徐越兰.碳钢焊条熔敷金属力学性能非线性神经网络组合预测[J].南京理工大学学报(自然科学版),2012,36(05):800.
 HUANG Jun,XU Yue-lan.Nonlinear Combination Prediction of Mechanical Properties of CarbonSteel Electrode Deposited Metal Based on Neural Network[J].Journal of Nanjing University of Science and Technology,2012,36(04):800.
[4]袁军堂,夏玲玲,汪振华,等.灌胶结合面对数控机床动态性能影响研究[J].南京理工大学学报(自然科学版),2012,36(06):0.
 YUAN Jun tang,XIA Ling ling,WANG Zhen hua,et al.Impact of Glued Joint on Dynamic Characteristics of CNC Machine Tool[J].Journal of Nanjing University of Science and Technology,2012,36(04):0.
[5]朱 红,陈清华,刘国岁.高速神经网络HS-K-WTA-2的研究[J].南京理工大学学报(自然科学版),2007,(01):89.
 ZHU Hong,CHEN Qing-hua,LIU Guo-sui.High-speed Neural Network HS-K-WTA-2[J].Journal of Nanjing University of Science and Technology,2007,(04):89.
[6]都洪基,苏炜宏,邓烽.一种基于神经网络的谐波电流抑制方法[J].南京理工大学学报(自然科学版),2003,(01):28.
 DuHongji SuWeihong DengFeng.Adaptive Approach to Detection of the Harmonic Currents on Artificial Neural Network[J].Journal of Nanjing University of Science and Technology,2003,(04):28.
[7]魏荣,卢俊国,李军,等.α阶时延逆系统的小波神经网络实现[J].南京理工大学学报(自然科学版),2001,(04):342.
 WeiRong LuJunguo LiJun WangZhiquan.Realization of α-order Time-delay Inverse Systems Based on Wavelet Neural Networks[J].Journal of Nanjing University of Science and Technology,2001,(04):342.
[8]聂伟荣,朱继南,赵玉霞.基于改进BP网络的地震动信号目标识别[J].南京理工大学学报(自然科学版),2000,(01):20.
 NieWeirong ZhuJinan ZhaoYuxia.Microseismic Signal Targets Identification Based on Improved BP Neural Networks[J].Journal of Nanjing University of Science and Technology,2000,(04):20.
[9]卢云许,陆宝春,张世琪.切片平均分子量神经网络预测模型研究及应用[J].南京理工大学学报(自然科学版),2000,(02):139.
 LuYunxu LuBaochun ZhangShiqi.The Research and Application of Neural Network Predicting Model of Molecule Weight of Casting Slice Belt[J].Journal of Nanjing University of Science and Technology,2000,(04):139.
[10]牛玉刚,杨成梧,赵建丛.基于多步预测的PID型神经网络控制[J].南京理工大学学报(自然科学版),2000,(03):199.
 NiuYugang YangChengwu ZhaoJiancong.A PID-like Neural Network Control Based on Multi-step Prediction[J].Journal of Nanjing University of Science and Technology,2000,(04):199.

备注/Memo

备注/Memo:
赵启林 男 27 岁 博士生
更新日期/Last Update: 2013-03-29