本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
文本提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。
A classification method based on fuzzy vector space model and radial basis function network is presented in this paper.
针对文本自动分类问题，提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。
Aimed at the problems of document automatic classification, a classification method is proposed based on fuzzy vector space model and RBF network.
并对该特征向量进行对数归一化，将归一化的特征向量作为径向基函数（RBF）神经网络的输入，在此基础上进行识别，达到较好的识别效果。
The normalized vector is used as the input of RBF NN, and target recognition is performed based on this, which leads to a satisfactory recognition result.
首先分析了支持向量机原理，随后引入一种改进的径向基核函数，在此基础上，提出了一种改进核函数的SVM模式分类方法。
The theory of SVM is studied at first, then an ameliorated RBF kernel function is presented, based on which an improved kernel function pattern classification method of SVM is put forward.
通过对基于多项式核函数和径向基核函数的支持向量机分类器进行比较，并且得到三种肝脏分类的识别率。
The classification accuracy of SVM based on polynomial and radial basis function kernel were compared, and the recognition accuracy of the three categories were obtained.
在实际工业数据上进行的实验结果表明，支持向量机模型对丙酮纯度具有良好的预测效果，性能优于反向传播神经网络和径向基网络模型。
The experimental results on the real industrial data demonstrate that the model based on SVM achieves good performance and has less prediction errors than those of BPNN and RBFNN models.
在实际工业数据上进行的实验结果表明，支持向量机模型对丙酮纯度具有良好的预测效果，性能优于反向传播神经网络和径向基网络模型。
The experimental results on the real industrial data demonstrate that the model based on SVM achieves good performance and has less prediction errors than those of BPNN and RBFNN models.
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