作　　者： ; ;

机构地区： 广东工业大学信息工程学院

出　　处： 《计算机仿真》 2008年第8期144-147,共4页

摘　　要： 为了改善对人工神经网络行为的认识和研究中的"黑匣子"式的难以处理的状态,基于RBF神经元模型的几何解释,提出了一种新的RBF神经网络分类算法,算法把RBF神经元看作是高维空间里的超球面,从而将神经网络训练问题转化为点集"包含"问题。同传统的RBF网络相比,算法能够自动地优化RBF网络中核函数的个数、中心和宽度,同时,省去了传统RBF神经网络输出层线性连接权的计算,简化了网络的学习过程,大大缩短了训练时间,并且通过实验证明了算法的有效性。 In order to reduce the difficulty in the process of knowing and researching neural networks, based on the geometrical representation of RBF neuron model, this paper proposes a new classification algorithm, which regards the neurons of RBF neural networks as a series of hyperspheres in the high - dimension space, Thus the training problem of neural networks can be transformed into the＂ including＂ problem of a point set. Compared with the classic RBF neural networks, the new algorithm has the ability of self - determining the number of hidden units and the centers and widths of the basis functions, At the same time, the new algorithm eliminates the computation of the linear combination of the outputs of all the RBF neurons and reduces the long training time and learning complexity. Computer simulation results show that the proposed neurai network is quite efficient.

关 键 词： 模式识别 神经网络 神经元 分类器

领　　域： [自动化与计算机技术] [自动化与计算机技术]