基于改进QPSO和RBF神经网络的文本分类方法

为提高文本分类的准确性，本文提出了一种基于量子PSO和RBF神经网络的新的文本分类方法.首先建立描述样本类别的关键词集合，并采用模糊向量空间模型建立每类样本的特征向量，然后采用RBF神经网络实施文本自动分类，采用改进的量子PSO优化RBF神经网络的参数，以提高其逼近能力.选取中国期刊网的部分文献作为实验数据，实验结果说明本文所提出方法的分类精准度与其他同类方法相比有明显的提高.

Document Classification Based on Improved QPSO and RBF Neural Networks

To enhance the accuracy of the text classification, a new method based on quantum PSO and RBF neural network is proposed. Firstly, it establishes the key words set to describe the classification of the samples, and uses fuzzy vector space model to build the feature vectors of every kind of sample, then automatically classifies the texts by RBF neural network, optimizes the parameters of RBF neural network by improved quantum PSO to enhance its approximation capability. The new method is proved by the classification of some documents in China periodical document database. The experiment shows that this method makes significant improvements in classification accuracy compared to other methods.