%0 Journal Article
%T Radial-basis-function neural network based on fast recursive algorithm and its application<br>基于快速回归算法的RBF神经网络及其应用
%A DU Da-jun
%A FEI Min-rui
%A LI Li-xiong
%A <br>杜大军
%A 费敏锐
%A 李力雄
%J 控制理论与应用
%D 2008
%I 
%X 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.
%K radial basis function neural network(RBFNN)
%K fast recursive algorithm(FRA)
%K orthogonal least squares
%K chaotic time series<br>径向基神经网络(RBFNN)
%K 快速回归算法
%K 正交最小二乘
%K 混沌时间序列
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=E76A82D3F25C94AF556310BD9E442E1F&yid=67289AFF6305E306&vid=C5154311167311FE&iid=94C357A881DFC066&sid=AC3946AB81989513&eid=CE16DE2ABCD4D365&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=10