| 摘要: |
| 分析传统BP算法存在的缺点,并针对这些缺点提出一种改进的BP学习算法.证明该算法在一定 条件下是超线性收敛的,并且该算法能够克服传统BP算法的某些弊端,算法的计算复杂度与简 单BP算法是同阶的.实验结果说明这种改进的BP算法是高效的、可行的. |
| 关键词: 前馈神经网络,BP学习算法,收敛性,超线性收敛. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家自然科学基金(No.69705001)资助. |
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| Super-Linearly Convergent BP Learning Algorithm for Feedforward Neural Networks |
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LIANG Jiu-zhen,HE Xin-gui,HUANG De-shuang
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| Abstract: |
| In this paper, some shortages of traditional BP learning algorithm are analyzed. To avoid these shortages, a modified BP learning algorithm is proposed. It is s hown that this algorithm is super-linearly convergent under certain conditions. This algorithm can overcome some shortages of traditional BP learning algorithm , and has the same order of computation complexity as the traditional BP algorit hm. Finally, two computing examples are given. Simulation results illustrate tha t this algorithm is highly effective and practicable. |
| Key words: Feedforward neural network, BP learning algorithm, convergence, super-linear c onvergence. |
