参考文献/References:
[1] Jacobs R A.Increased rates of convergence through learning rate adaptation.Neural Networks,1988,1(4):295~308
[2] Allred L G,Kelly G E.Supervised learning techniques for backpropagation networks.In:Proceedings of Intern Joint Conf on Neural Networks,I,San Diego,1990.702~709
[3] Vogel T P.Accelerating the convergence of the backpropagation method.Biol Cybern,1988,59:257~263
[4] 陈国安.前馈网络训练理论及其在信道均衡中的应用:[学位论文].南京:东南大学无线电系,1994
[5] 尤肖虎,陈国安,程时昕.Dynamic learning rate optimization of the backpropagation algorithm.IEEE Transactions on Neural Networks,1995,6(3):669~677
[6] 尤肖虎,陈国安,程时昕.Acceleration of backpropagation learning using optimized learning rate and momentum.Electronics Letters,1993,29(14):1288~1290
[7] 陈国安,尤肖虎.Learning rate optimization of backpropagation training using first order derivative.In:International Conference on Neural Networks and Signal Processing,Guanzhou China,1993
相似文献/References:
[1]伍建国,孙庆鸿,毛海军,等.基于BP神经网络模型的磨床部件动态灵敏度分析[J].东南大学学报(自然科学版),2002,32(4):601.[doi:10.3969/j.issn.1001-0505.2002.04.014]
Wu Jianguo,Sun Qinghong,Mao Haijun,et al.Dynamic sensitivity analysis of grinder parts based on the BP neural network model[J].Journal of Southeast University (Natural Science Edition),2002,32(4):601.[doi:10.3969/j.issn.1001-0505.2002.04.014]
[2]毛海军,孙庆鸿,陈南,等.应用BP神经网络模型实现内圆磨床主要零件的快速反应设计[J].东南大学学报(自然科学版),2003,33(3):316.[doi:10.3969/j.issn.1001-0505.2003.03.017]
Mao Haijun,Sun Qinghong,Chen Nan,et al.Rapid response design of the main parts of internal grinder based on BP neural network model[J].Journal of Southeast University (Natural Science Edition),2003,33(4):316.[doi:10.3969/j.issn.1001-0505.2003.03.017]
[3]毛海军,孙庆鸿,陈南,等.基于BP神经网络模型的机床大件结构动态优化方法及其应用研究[J].东南大学学报(自然科学版),2002,32(4):594.[doi:10.3969/j.issn.1001-0505.2002.04.012]
Mao Haijun,Sun Qinghong,Chen Nan,et al.Dynamic optimization of large parts of machine tool based on BP neural network model[J].Journal of Southeast University (Natural Science Edition),2002,32(4):594.[doi:10.3969/j.issn.1001-0505.2002.04.012]
[4]王建,邓卫,赵金宝.基于改进型贝叶斯组合模型的短时交通流量预测[J].东南大学学报(自然科学版),2012,42(1):162.[doi:10.3969/j.issn.1001-0505.2012.01.030]
Wang Jian,Deng Wei,Zhao Jinbao.Short-term freeway traffic flow prediction based on improved Bayesian combined model[J].Journal of Southeast University (Natural Science Edition),2012,42(4):162.[doi:10.3969/j.issn.1001-0505.2012.01.030]