差分进化训练径向基函数网络

Ting Yu, Xingshi He
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引用次数: 75

摘要

本文提出了一种新的进化算法——差分进化算法(DE),用于训练径向基函数(RBF)网络,涉及网络结构的自动配置。对数据集进行分类任务:Iris, Wine, New-thyroid和Glass,以衡量神经网络的性能。与Matlab神经网络工具箱中的标准RBF训练算法相比,DE实现了更合理的RBF网络结构。由此产生的网络具有较强的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Training radial basis function networks with differential evolution
In this paper, Differential Evolution (DE) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, New-thyroid and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, DE achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.
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