中文摘要：

提出了一种基于Hough变换优化的RBF神经网络模式识别新方法，该方法把Hough变换应用于RBF神经网络的参数确定中，实现了RBF神经网络的隐层节点数和数据中心值的自适应获取，提高了RBF神经网络的泛化能力。仿真结果表明：此改进的RBF网络用于模式识别中具有识别能力强，计算量小，识别速度快的优点，具有广阔的应用推广前景。

英文摘要：

A new method of pattern recognition based on optimized RBF neural networks using Hough Transform was improved. Hough Transform was applied to the parameters selection and the adaption of the number and position of data centers of RBF neural networks that were realized in this method. Consequently, RBF neural networks designed with this method could generalize well. Experiments results show that the improved RBF neural networks applied in pattern recognition turn out to be a higher accuracy, faster and elegant way. The method possesses high value being generalized.