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On-line Access: 2020-06-12
Received: 2019-10-31
Revision Accepted: 2020-01-13
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Citations: Bibtex RefMan EndNote GB/T7714
Yi-fei Pu, Jian Wang. Fractional-order global optimal backpropagation machine trained by an improved fractional-order steepest descent method[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1900593 @article{title="Fractional-order global optimal backpropagation machine trained by an improved fractional-order steepest descent method", %0 Journal Article TY - JOUR
用改进的分数阶最速下降法训练分数阶全局最优反向传播机1四川大学计算机学院,中国成都市,610065 2中国石油大学(华东)理学院,中国青岛市,266580 摘要:本文介绍采用改进的分数阶最速下降法(FSDM)训练分数阶全局最优反向传播机。该反向传播机是一种分数阶反向传播神经网络(FBPNN)。分数阶反向传播神经网络是反向传播神经网络(BPNNs)大家族中一个先进的分数阶分支,它不同于绝大多数传统一阶最速下降法训练的经典一阶BPNNs。本文提出的FBPNN反向增量搜索在其均方误差近似分数阶偏导数的负方向进行。首先,从数学上描述用改进FSDM训练的FBPNN理论概念。然后,详细给出FBPNN分数阶全局最优收敛性的数学证明,分析神经网络结构构建以及分数阶多尺度全局寻优问题。最后,通过实验比较FBPNN和经典一阶BPNN的性能:包括函数逼近、分数阶多尺度全局寻优以及基于实际数据的全局搜索和误差拟合能力比对。相比经典一阶BPNN,FBPNN最主要优点是具有更高效的全局寻优能力,能够判定全局最优解。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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