计算机科学 ›› 2012, Vol. 39 ›› Issue (Z11): 108-110.
• 服务化的科研成果 • 上一篇 下一篇
张自敏，樊艳英，陈冠萍
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摘要： BP算法是应用最广泛的人工神经网络算法，但标准的I3P算法存在收敛速度慢及易陷入局部极小值的缺陷。针对这些缺陷，综合利用附加动量和变学习率的方法对BP算法加以改进。通过改进的BP算法用MATLAB对2001-2010年广西GDP数据进行了仿真，结果表明，改进的BP算法的收敛速度和预测精度均优于标准I3P算法。
关键词: BP算法，人工神经网络，改进BP算法，MATLAB
Abstract: BP (back-propagation, BP) algorithm is a way widely used in artificial neural networks. However, this algorithm exposes its defects in tardiness of convergence rate and easily falling into partial minimum. Thus,aiming at those defects,measures of improved BP algorithm are adopted by additional momentum and variable learning rate, and the simulation of MATLAB to Guangxis GDP from 2001 to 2010 shows that the multiple BP algorithm is superior to the standard BP algorithm in both convergence rate and accuracy.
Key words: Back-Propagation algorithm, Artificial neural networks, Improved Back-Propagation algorithm, MATLAB
张自敏，樊艳英，陈冠萍. 改进的BP神经网络在地方GDP预测中的应用[J]. 计算机科学, 2012, 39(Z11): 108-110. https://doi.org/
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