提出了一种新的确定径向基函数中心的方法。
A new method that determines radial basis function centers is proposed.
径向基函数网络的性能在很大程度上取决于径基函数中心位置的选取。
Performance of radial basis function network highly depends on the locations of radial basis function centers.
讨论了径向基函数中心的选取，构造了改进的RBF网络对训练样本和测试样本进行识别。
The choice of the center of radial basis function, constructing an improved RBF network and its application to recognize the trained samples and test samples were discussed.
该模型首先采用改进的最近邻聚类算法确定径向基函数中心，接着应用递推最小二乘法训练网络的权值。
The model USES an improved nearest-neighbor clustering algorithm to select the RBF center, and a recursive least square algorithm to train weights of the RBF neural network.
针对R BF训练算法中径向基函数中心确定的困难，在分析比较目前较好的算法基础上，提出一种新的训练算法。
The major contributions of the dissertation are stated as follows: 1 a new training algorithm is proposed to obtain RBF centers.
该方法通过选择径向基函数中心、确定神经网络隐层神经元的数目和调整每一层的权值和阈值，对由于PSD非线性产生的误差进行修正。
The nonlinear error of PSD was modified by choosing the centre of RBF, ascertaining the number of neural cell of the neural network and adjusting the weight and the threshold of each hiberarchy.
由于RBF网络和模糊推理系统具有函数等价性，采用模糊经验值方法选取网络中心值和基函数数目。
Due to the function equivalence between RBF neural networks and fuzzy inference system, fuzzy experience method is adopted to select the centers and the numb er of basis function networks.
在径向基函数神经网络中，隐层中心的数量和位置的选择是整个网络性能优劣的关键，直接影响网络的分类能力。
The choice of quantity and position of hidden layer radial basis functions is very important and directly affects the goodness of fit of overall network classification ability.
在径向基函数神经网络中，隐层中心的数量和位置的选择是整个网络性能优劣的关键，直接影响网络的分类能力。
The choice of quantity and position of hidden layer radial basis functions is very important and directly affects the goodness of fit of overall network classification ability.
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