| 基于优化的RBF神经网络模式识别新方法 |
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| 引用本文： | 李国友,姚磊,李惠光,吴惕华.基于优化的RBF神经网络模式识别新方法[J].系统仿真学报,2006,18(1):181-184. |
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| 作者姓名： | 李国友 姚磊 李惠光 吴惕华 |
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| 作者单位： | 1. 燕山大学,河北秦皇岛,066004
2. 河北省科学院,河北石家庄,050000 |
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| 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。
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| 关 键 词： | Hough变换 RBF神经网络 函数逼近 模式识别 泛化能力 |
| 文章编号： | 1004-731X（2006）01-0181-04 |
| 收稿时间： | 2004-09-27 |
| 修稿时间： | 2005-10-23 |
A New Method of Pattern Recognition Based on Optimized RBF Neural Networks |
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| LI Guo-you,YAO Lei,LI Hui-guang,WU Ti-hua.A New Method of Pattern Recognition Based on Optimized RBF Neural Networks[J].Journal of System Simulation,2006,18(1):181-184. |
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| Authors: | LI Guo-you YAO Lei LI Hui-guang WU Ti-hua |
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| Institution: | 1 .Yanshan University, Qinhuangdao 066004, China; 2.Academy of Sciences of Hebei, Shijiazhuang 050000, China |
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| Abstract: | 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. |
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| Keywords: | Hough Transform RBF neural networks function approach pattern recognition generalization ability |
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