基于广义回归神经网络的单位运营状况分类
发布时间：2019-05-23 07:16
【摘要】：单位的运营状况会直接影响股东和广大人民的利益,针对运营状况可以使用广义回归神经网络进行分类。由于广义回归神经网络中径向基函数的扩展参数Spread的选取会导致分类的准确率,提出了一种果蝇优化算法优化参数Spread的分类模型。充分利用了果蝇优化算法的寻优能力,将优化后的参数代入到广义回归神经网络中对单位的财务数据进行运营状况的分类。结果表明,与广义回归神经网络做比较,优化后的网络模型对数据的分类可以达到很高的准确率,在相关领域的分类上有非常大的实用性。
[Abstract]:The operating condition of the unit will directly affect the interests of shareholders and the broad masses of the people, and the general regression neural network can be used to classify the operating situation. Because the selection of the extended parameter Spread of the radial basis function in the generalized regression neural network will lead to the accuracy of classification, a classification model of the optimization parameter Spread of Drosophila melanogaster optimization algorithm is proposed. Making full use of the optimization ability of Drosophila melanogaster optimization algorithm, the optimized parameters are substituted into the generalized regression neural network to classify the financial data of the unit. The results show that compared with the generalized regression neural network, the optimized network model can achieve high accuracy in the classification of data, and has great practicability in the classification of related fields.
【作者单位】： 中北大学理学院;
【基金】：国家自然科学基金资助项目(61275120)
【分类号】：TP183
，
本文编号：2483708
[Abstract]:The operating condition of the unit will directly affect the interests of shareholders and the broad masses of the people, and the general regression neural network can be used to classify the operating situation. Because the selection of the extended parameter Spread of the radial basis function in the generalized regression neural network will lead to the accuracy of classification, a classification model of the optimization parameter Spread of Drosophila melanogaster optimization algorithm is proposed. Making full use of the optimization ability of Drosophila melanogaster optimization algorithm, the optimized parameters are substituted into the generalized regression neural network to classify the financial data of the unit. The results show that compared with the generalized regression neural network, the optimized network model can achieve high accuracy in the classification of data, and has great practicability in the classification of related fields.
【作者单位】： 中北大学理学院;
【基金】：国家自然科学基金资助项目(61275120)
【分类号】：TP183
，
本文编号：2483708
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