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自底向上优化神经网络的方法

A Heuristic Approach for Improving Performance of Neural Network with the Cascade-Correlation
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摘要 引荐了一种自动优化神经网络的新方法。这种启迪方法综合采用了相关有效算法,通过快速自底向上构造神经网络算法,可以获得优化结构的神经网络,即时选定参数算法动态优化神经网络的学习参数,并且快速交叉校验算法为解决过度适应问题提供了捷径。实验证明,这种启迪方法能自动有效地优化神经网络,与其它算法相较而言,具有更好的归纳性能、优化的网络结构和更快的学习速度。 A new approach for the automated design of optimal neural network is presented.The heuristic approach employs several efficient algorithms,such as construction of optimal network architecture via fast cascade-correlation,dynamic optimization of learning parameters via simultaneous determination,avoiding overfitting problem via fast cross-validation.The experimental results show that the heuristic approach can automatically design optimal neural network with good generalization capability and optimal network and short training time in comparison with other algorithms.
作者 杨钟瑾
机构地区 广东商学院信息学院
出处 《计算机工程与应用》 CSCD 北大核心 2006年第23期34-37,77,共5页 Computer Engineering and Applications
关键词 神经网络 瀑流关联 自底向上 学习参数优化 交叉校验 Neural Network,cascade-correlation,bottom-up,learning parameters optimization,cross-validation
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献16

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二级参考文献54

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共引文献14

计算机工程与应用
2006年 第23期

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