中文摘要：

提出一种基于构造型神经网络的最大密度覆盖分类算法，以便更加有效地解决模式识别的问题。首先，引入一个密度估计函数，用该函数对样本数据进行聚类分析，找出同类样本中具有最大密度的样本数据点，然后，在特征空间里作超平面与球面相交，得到1个球面覆盖领域，从而将神经网络训练问题转化为点集覆盖问题。该算法的特点是直接对样本数据进行处理，有效地克服了传统神经网络训练时问长、学习复杂的问题，同时也考虑了神经网络规模的优化问题。计算机仿真实验结果证实了该算法的有效性。

英文摘要：

A new maximum density covering classification algorithm based on constructive neural networks was proposed, which can be used to resolve the problem of pattern recognition more effectively. Firstly, a density estimating function was proposed, which was used for clustering analysis of sample data, and a sample data point with the maximum density was found. Then, a super-plane was made to intersect a sphere in the characteristics of the space, and a spherical covering area was obtained, by which the training problem of neural networks can be transformed into the covering problem of a point set. The characteristic of the algorithm is that the sample data can be handled directly. This new algorithm can reduce the long training time and learning complexity of traditional neural networks. The optimization of the neural network is also considered. The simulation results show that the proposed neural network is quite efficient.