主成分分析法在我国税收预测中的应用

Application in Tax Forecasting of Main Component Method

  • 摘要: 神经网络非常适用于复杂非线性系统的处理,已被广泛应用于经济预测中.针对经济预测中指标多、历史数据不足从而大大削弱神经网络的泛化能力问题,本文利用主成分分析法建立了税收的预测模型.结果显示:经主成分分析之后再进行预测,能从根本上降低神经网络规模,提高了神经网络的泛化能力.

     

    Abstract: Artifical neural network is suitable for non-linear systems and is popularly applied in economic forecasting.But there are so many indexes and few historical numbers in ecoomic forecasting that weaken the generalization performance of the neural network.This article puts forward a tax forecasting model by use of main component method.Results indicate that reforecasting after the main component method can drop the scope of the neural network and improve its generalization performance.

     

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