中文摘要：

针对一般RBF神经网络在学习过程中网络结构不能改变的问题，提出一种动态RBF神经网络结构设计方法．算法的实质是利用敏感度分析法（SA）对神经网络模型的输出进行分析，通过判断隐含层神经元的输出对整个网络输出的影响，删除RBF隐含层中冗余的神经元，实现对神经网络的动态修剪．非线性函数逼近结果及动态系统建模结果表明，该动态RBF神经网络具有较好的性能；与最小RBF（MRBF）神经网络相比，采用所提算法能得到更小的检测误差和更短的训练时间，最终网络结构紧凑．

英文摘要：

Due to the unchangable structure of conventional RBF （radial basis function） neural networks in the learning process, a new structure of dynamic RBF neural network is designed. The presented algorithm is based on the sensitivity analysis （SA） on the model output of neural network. The redundant nodes in the hidden layer is pruned by judging the effect of the output of the nodes in the hidden layer on the whole network output, and thus the dynamic pruning of the neural network is accomplished. The results of approximating non-linear functions and modeling the non-linear systems show the perfect performance of this algorithm, and comparing with the Minimal RBF （MRBF） neural network, the proposed SARBF can achieve the smaller testing error, the shorter training time and a compact neural network structure.