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广义rbf神经网络
相关语句
  generalized radial basis function neural networks
    On the prediction of chaotic time series using a new generalized radial basis function neural networks
    一种新型广义RBF神经网络在混沌时间序列预测中的研究
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  “广义rbf神经网络”译为未确定词的双语例句
    Application Study on BP Network and Generalized RBF Network in Estimating Distribution Model of Mechanical Products
    BP神经网络与广义RBF神经网络在产品寿命分布模型识别中的应用研究
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    A New Type of General RBF Neural Network and Its Training Method
    一种新型的广义RBF神经网络及其训练方法
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    Simulation results show that the new generalized RBF networks can accurately predict chaotic time series. It provides an attractive approach to study the properties of complex nonlinear system model and chaotic time series.
    结果表明,所提出的新型广义RBF神经网络模型能快速、精确地预测混沌时间序列,是研究复杂非线性动力系统辨识和控制的一种有效方法.
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Radial basis function (RBF) networks have been widely used for function approximation and pattern classification as an alternative to conventional feedforward neural networks. A novel generalized RBF neural network model is presented. The form of RBF is determined by a generator function, and then an easily implementable gradient decent learning algorithm for training the new generalized RBF networks is given. Simultaneously, a fast dynamic learning algorithm based on Kalman filter is also proposed to improve...

Radial basis function (RBF) networks have been widely used for function approximation and pattern classification as an alternative to conventional feedforward neural networks. A novel generalized RBF neural network model is presented. The form of RBF is determined by a generator function, and then an easily implementable gradient decent learning algorithm for training the new generalized RBF networks is given. Simultaneously, a fast dynamic learning algorithm based on Kalman filter is also proposed to improve the performance and accelerate the convergence speed of the new generalized RBF networks. The generalized RBF neural networks based on Kalman filtering dynamic learning algorithm is then applied to the chaotic time series prediction on the Mackey-Glass equation and the Henon map to test the validity of this proposed model.Simulation results show that the new generalized RBF networks can accurately predict chaotic time series. It provides an attractive approach to study the properties of complex nonlinear system model and chaotic time series.

提出了一种新颖的广义径向基函数神经网络模型,其径向基函数(RBF)的形式由生成函数确定.然后,给出了易实现的梯度学习算法,同时为了进一步提高网络的收敛速度和网络性能,又给出了基于卡尔曼滤波的动态学习算法.为了验证网络的学习性能,采用基于卡尔曼滤波算法的新型广义RBF网络预测模型对Mackey-Glass混沌时间序列和Henon映射进行了仿真.结果表明,所提出的新型广义RBF神经网络模型能快速、精确地预测混沌时间序列,是研究复杂非线性动力系统辨识和控制的一种有效方法.

A new type of general RBF neural network model,which replaces weight of outer layer with weight function,i.e.replaces linear weight with high order function,is proposed.Network training method is brought forward.Parameters selection such as hide layer width and power of weight function,which have effect on approximation precision and training time of the network,are investigated through simulation.The results indicate the general RBF network has better approximation ability and faster calculation speed than...

A new type of general RBF neural network model,which replaces weight of outer layer with weight function,i.e.replaces linear weight with high order function,is proposed.Network training method is brought forward.Parameters selection such as hide layer width and power of weight function,which have effect on approximation precision and training time of the network,are investigated through simulation.The results indicate the general RBF network has better approximation ability and faster calculation speed than traditional RBF neural network,which promotes a good prospect in the field of system identification and control.

提出一种新型的广义RBF神经网络模型,将径向基输出权值改为权函数,采用高次函数取代线性加权。给出网络学习方法,并通过仿真分析研究隐单元宽度、权函数幂次等参数的选取对网络逼近精度以及训练时间的影响。结果表明,和传统的RBF神经网络相比,该网络具有良好的逼近能力和较快的计算速度,在系统辨识和控制中具有广阔的应用前景。

 
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