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Computer Engineering ›› 2012, Vol. 38 ›› Issue (23): 173-176,180. doi: 10.3969/j.issn.1000-3428.2012.23.043

• Networks and Communications • Previous Articles     Next Articles

Improvement Algorithm of Levenberg-Marquardt Back Propagation for Saddle Point in Neural Network

LI Jiong-cheng, XIAO Heng-hui, LI Gui-yu   

  1. (Key Wireless Network Optimization Center of Guangzhou, Guangdong Planning and Designing Institute of Telecommunications Co., Ltd., Guangzhou 510630, China)
  • Received:2012-01-17 Online:2012-12-05 Published:2012-12-03

神经网络中处理鞍点的LMBP改进算法

李炯城,肖恒辉,李桂愉   

  1. (广东省电信规划设计院有限公司广州市无线网络优化重点工程中心,广州 510630)
  • 作者简介:李炯城(1972-),男,博士,主研方向:最优化算法,无线通信技术,软件系统架构;肖恒辉,博士;李桂愉,硕士
  • 基金资助:
    广东省教育部产学研结合基金资助项目(2009B090300393);广州市软件(动漫)产业发展基金资助项目(2060404)

Abstract: Aiming at the Levenberg-Marquardt Back Propagation(LMBP) algorithm of neural network sometimes converges to the saddle point during training process, an improved LMBP algorithm which can overcome the saddle point effectively is proposed. All eigenvectors for all the positive eigenvalue of Jacobi matrix are calculated as new searching directions. The improved LMBP algorithm is proved that it can get out of saddle point, and it iterates to minima effectively by an example of comparing with the traditional LMBP algorithm and the improved one.

Key words: neural network, Levenberg-Marquardt Back Propagation(LMBP) algorithm, saddle poin, Jacobi matrix, Hessian matrix, Gauss- Newton algorithm

摘要: 针对目前神经网络中的Levenberg-Marquardt反向传播(LMBP)算法在训练过程中有可能迭代到鞍点的问题,提出一种能有效克服鞍点的LMBP改进算法。计算鞍点处雅克比矩阵的正特征值对应的特征向量并将其作为新的搜索方向。通过实例对比传统LMBP算法与改进LMBP算法的效果,证明改进的算法能有效地脱离鞍点并进一步收敛到极小点处。

关键词: 神经网络, Levenberg-Marquardt反向传播算法, 鞍点, 雅克比矩阵, 海森矩阵, 高斯-牛顿法

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