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中文摘要：
对径向基函数（RBF）神经网络在数据分类中的应用进行了研究．提出一种应用于模式识别的动态RBF训练算法，该算法使用区域映射误差函数并结合资源分配网络（RAN）的“新性”（novelty）条件动态调整网络的隐层节点数，从而可以更加有效地进行模式识别．二分类样本和建筑材料CaO-Al2O3-SiO2系统仿真表明，该改进算法使误差下降更快，减少了训练次数，可以获得精简的网络结构，从而使网络具有较高的泛化能力．
英文摘要：
The application of radial basic function （RBF） neural network in the data classification is studied. A new dynamic training algorithm for RBF network used in pattern recognition is proposed. It uses the regional mapping error function and the novelty condition of the resource-allocating network （RAN） to dynamically adjust the nodes in the hidden layer of the network, and makes the pattern recognition more efficient. By the simulation result of the modeling of synthetic two-class problem and CaO-Al2O3-SiO2 system, it is proven that the algorithm can make the descending speed of error more quick and shorten the training times, thus the network with the concise structure is obtained and better generalization is achieved.
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