一种提高前向神经网络泛化性能的新算法
A New Algorithm to Improve the Generalization Ability of Feed-forward Neural Networks
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摘要: 提出了一种利用遗传算法优化前向神经网络的结构和正则项系数的混合学习算法．将该方法与附加动量的BP算法、固定正则项系数神经网络方法进行比较．数值结果显示该方法具有精度高、学习收敛速度快和泛化能力高等优点．Abstract: A hybrid learning approach is presented in which genetic algorithms are used to optimize both the network architecture and the regularization coefficient.Comparison is made among this approach,the back-propagation(BP) algorithm with momentum term and the BP algorithm with fixed regularization coefficient.Numerical results demonstrate that the proposed approach is of highly computational accuracy,quickly convergent speed,and high(generalization) capability.
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