ODE-LM: A hybrid training algorithm for feedforward neural networks

被引：0
作者：
机构：
[1] Department of Mathematics, Xidian University
[2] National Lab of Radar Signal Processing, Xidian University
来源：
Zhang, L. (lzhang@xidian.edu.cn) | 1600年 / Springer Verlag卷 / 215期
关键词：
Differential evolution; Feedforward neural network; Levenberg-Marquardt method; Orthogonal crossover;
D O I：
10.1007/978-3-642-37835-5_17
中图分类号：
学科分类号：
摘要：
A hybrid training algorithm named ODE-LM, in which the orthogonal differential evolution (ODE) algorithm is combined with the Levenberg-Marquardt (LM) method, is proposed to optimize feedforward neural network weights and biases. The ODE is first applied to globally optimize the network weights in a large space to some extent (the ODE will stop after a certain generation), and then LM is used to further learn until the maximum number of iterations is reached. The performance of ODE-LM has been evaluated on several benchmarks. The results demonstrate that ODE-LM is capable to overcome the slow training of traditional evolutionary neural network with lower learning error. © Springer-Verlag Berlin Heidelberg 2014.
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页码：187 / 198
页数：11
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