说明：  一种基于蚁群聚类的径向基神经网络 提出了一种基于蚁群聚类算法的径向基神经网络. 利用蚁群算法的并行寻优特征和挥发系 数方法的自适应更改信息量的能力,并以球面聚类的方式确定了径向基神经网络中基函数的位置, 同时通过比较隐层神经元的相似性、合并相似性较为接近的2 个神经元来约简隐含层的神经元,以 达到简化径向基神经网络结构的目的. 实验比较了几种不同聚类算法的径向基神经网络,结果表 明,所提神经网络的整体训练时间至少可缩短40 ％ ,学习的准确率可提高1 ％以上,而且网络结构 更加精简.
(An Ant clustering RBFNN presents a clustering algorithm based on the ant colony RBFN Liaison. Ant use the parallel algorithm optimization features and volatile coefficient method of adaptive changes in the volume of information, spherical cluster and the method of RBF neural network-based functions of the position, By comparing the same time hidden layer neurons of similarity, Merger similarity is closer to the two neurons to Jane about the hidden layer neurons, to achieve simplification RBF neural network structure purposes. Experimental comparison of several different clustering algorithm RBF neural network, The results show that the neural network's overall training time can be shortened at least 40%. Learning the accuracy rate can be increased by 1%, and the network is more streaml)