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Received:June 24, 2011 Revised:July 17, 2011
Received:June 24, 2011 Revised:July 17, 2011
中文摘要: 由于RBF 神经网络结构简单、输出与初始权值无关、自适应、可调参数少等特点,提出了利用交叉验证法寻最优参数SPREAD 值,构建最优RBF 神经网络模型并结合MIV 算法用于变量筛选。通过实例检验了模型的有效性,也使该方法具有较好的稳定性和应用性。
Abstract:Because of the Characteristics of RBF neural network structure is simple, output and initialized weights irrelevant, adaptive, less adjustable parameter etc. This paper proposes using the method of cross validation to find the optimal parameter value of SPREAD, constructs the optimal RBF neural network model and combines the algorithm of MIV to use for variables screening. Through the example test the validity of the model, also make the method has better ability of stability and applied.
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基金项目:巢湖学院自然科学研究资助项目(XLY-201101)
| Author Name | Affiliation |
| XU Fu-Qiang | Department of Mathematics, Chaohu College, Chaohu 238000, China |
| LIU Xiang-Guo | Department of Mathematics, Chaohu College, Chaohu 238000, China |
| Author Name | Affiliation |
| XU Fu-Qiang | Department of Mathematics, Chaohu College, Chaohu 238000, China |
| LIU Xiang-Guo | Department of Mathematics, Chaohu College, Chaohu 238000, China |
引用文本：
徐富强,刘相国.基于优化的RBF 神经网络的变量筛选方法.计算机系统应用,2012,21(3):206-208
XU Fu-Qiang,LIU Xiang-Guo.Variables Screening Methods Based on the Optimization of RBF Neural Network.COMPUTER SYSTEMS APPLICATIONS,2012,21(3):206-208
徐富强,刘相国.基于优化的RBF 神经网络的变量筛选方法.计算机系统应用,2012,21(3):206-208
XU Fu-Qiang,LIU Xiang-Guo.Variables Screening Methods Based on the Optimization of RBF Neural Network.COMPUTER SYSTEMS APPLICATIONS,2012,21(3):206-208
