| 引用本文: | 杜大军,费敏锐,李力雄.基于快速回归算法的RBF神经网络及其应用[J].控制理论与应用,2008,25(5):827~830.[点击复制] |
| DU Da-jun,FEI Min-rui,LI Li-xiong.Radial-basis-function neural network based on fast recursive algorithm and its application[J].Control Theory and Technology,2008,25(5):827~830.[点击复制] |
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| 基于快速回归算法的RBF神经网络及其应用 |
| Radial-basis-function neural network based on fast recursive algorithm and its application |
| 摘要点击 1859 全文点击 1194 投稿时间：2007-01-16 修订日期：2007-12-22 |
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| DOI编号 |
| 2008,25(5):827-830 |
| 中文关键词 径向基神经网络(RBFNN) 快速回归算法 正交最小二乘 混沌时间序列 |
| 英文关键词 radial basis function neural network(RBFNN) fast recursive algorithm(FRA) orthogonal least squares chaotic time series |
| 基金项目 国家自然科学基金资助项目(60774059, 60834002); 上海市科委资助项目(061107031, 06ZR14131, 08XD14018); 上海市教委曙光计划跟踪项目(06GG10). |
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| 中文摘要 |
| 针对径向基神经网络(RBFNN)中存在的径向基函数中心的数目及其位置难以确定的问题, 提出了一种新型的基于快速回归算法(FRA)的RBFNN. 采用快速回归算法, 不但能够确定RBF的中心和中心个数, 而且能够求出隐含层到输出层的权重. 通过一元函数拟合和Mackey-Glass混沌时间序列预测的仿真, 验证了该网络的有效性与实用性. |
| 英文摘要 |
| Considering the difficulty in selecting the numbers and determining the locations of the centers of radial basis functions (RBF) in the RBF neural network (RBFNN), a novel RBFNN is proposed based on the fast recursive algorithm (FRA). Using FRA, we can determine the numbers and locations of the centers, and derive the weights between the hidden layer and the output layer. The new RBFNN is used to fit a single-variable function curve and predict the Mackey-Glass chaotic time series. The simulation results demonstrate the effectiveness and practicability. |
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