| 基于快速回归算法的RBF神经网络及其应用 |
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| 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. |
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| 作者姓名： | 杜大军 费敏锐 李力雄 |
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| 作者单位： | 上海大学,机电工程与自动化学院,上海市电站自动化技术重点实验室,上海,200072 |
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| 基金项目： | 国家自然科学基金，上海市科委资助项目，上海市教委曙光计划跟踪项目 |
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| 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F1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.
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| 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 |
| 收稿时间： | 2007/1/16 0:00:00 |
| 修稿时间： | 2007/12/22 0:00:00 |
Radial-basis-function neural network based on fast recursive algorithm and its application |
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| DU Da-jun,FEI Min-rui and LI Li-xiong.Radial-basis-function neural network based on fast recursive algorithm and its application[J].Control Theory & Applications,2008,25(5):827-830. |
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| Authors: | DU Da-jun FEI Min-rui and LI Li-xiong |
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| Affiliation: | Shanghai Key Laboratory of Power station Automation Technology, School of Mechatronical Engineering & Automation, Shanghai University, Shanghai 200072, China;Shanghai Key Laboratory of Power station Automation Technology, School of Mechatronical Engineering & Automation, Shanghai University, Shanghai 200072, China;Shanghai Key Laboratory of Power station Automation Technology, School of Mechatronical Engineering & Automation, Shanghai University, Shanghai 200072, China |
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| Abstract: | 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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| Keywords: | radial basis function neural network(RBFNN) fast recursive algorithm(FRA) orthogonal least squares chaotic time series |
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