为了克服传统神经网络算法在处理分类问题时训练时间长、泛化能力弱的不足，提出了一种新的基于构造型神经网络覆盖分类算法，该算法通过在超球面上对样本数据进行聚类分析，找出同类样本中未被覆盖样本的最大密度点，然后在特征空间里做超平面与球面相交，得到球面领域覆盖，从而将神经网络训练问题转化为点集覆盖问题，同时也考虑了神经网络规模的优化问题。实验结果证明了该算法的有效性。

In order to overcome the shortcoming of the longtime training and the frail generalization power of classical neural networks, this paper proposes a new covering classification algorithm based on constructive neural networks. The algorithm starts with the sample data directly and clustering analysis is executed on a hypersphere to find a sample with the max density, and then the intersection between the positive half-space of the hyperplane and sphere, called“sphere neighborhood”, is obtained, by which the training problem of neural networks may be transformed into the covering problem of point sets. Thus the new algorithm can reduce the traditional learning complexity. At the same time, the optimization of the neural network is also considered and computer simulation results show that the proposed neural network is quite efficient.