This paper proposes a text classification method based on Cloud Theory and neural network structure decision tree.
提出一种基于云理论和神经网络构造决策树的文本分类方法。
It adopts cloud neural network to study the cloud mapping relationship between variables, so as to generate cloud decision tree.
运用云神经网络学习变量间的云映射关系，从中生成云决策树。
By using neural networks as the approach for whitening system cloud gray model, the system cloud gray neural network models SCGNNM (1, 1), were proposed in this paper.
利用人工神经网络的方法实现系统云灰色模型的参数白化，提出了系统云灰色神经网络模型SCGNNM(1,1)，并给出了相应的学习算法。
To select feature samples in circuit fault diagnosis, we propose a method of cloud-sample generation, and apply it to artificial-neural-network training and recognition.
为了选择电路故障诊断中的特征样本，提出了产生云样本的方法，并用于神经网络的训练和识别。
To select feature samples in circuit fault diagnosis, we propose a method of cloud-sample generation, and apply it to artificial-neural-network training and recognition.
为了选择电路故障诊断中的特征样本，提出了产生云样本的方法，并用于神经网络的训练和识别。
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