基于概率神经网络和K-means算法的纳税评估

纳税评估工作是一项难于建立准确数学模型的复杂系统,同时又是一个典型的模式识别问题。用神经网络方法进行纳税评估有其独特的优越性。运用PNN算法很大程度地依赖于训练样本对象的选取。选取的这些样本能否反映总体的信息特征决定了分类器的识别效果。文章运用K-means算法对纳税人信息样本进行聚类,找出聚类中心点,以此为基础来选择样本作为PNN的训练样本,从而达到对PNN算法的优化。研究结果表明这种改进后的PNN算法分类效果好,对于纳税评估有其应用价值。

Tax assessment based on PNN and K- means algorithm

Tax assessment is a complex system which is hard to build a accurate mathematical model,furthermore,it is a typical recognition problem of model.to do tax assessment by using probabilistic neural network has its special advantages.the use of PNN algorithm depends greatly on the choosing of sample.whether the chosen sample can reflect the general information characteristics determines the recognition effects of classifying machine.In this article,K-means method is used to get the cluster center of tax payers’ information sample to choose sample as the training sample of PNN,thus reaching optimization of PNN algorithm.The research result shows that the classifying effects of improved PNN algorithm is good and has its values of tax assessment.