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.
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。
A two-level learning method combining improved immune algorithm and least square method was proposed to design a radial basis function (RBF) network.
结合改进的免疫算法和最小二乘法，提出了一种设计径向基函数(RBF)网络的两级学习方法。
Finally, the benchmark of function simulation shows that the precision and size of network are as the same as GAP-RBF, but the learning speed modified GAP-RBF is improved.
最后，函数模拟实验表明，所提出的算法在保留了原gap - R BF算法较高的精确度与紧凑的网络规模的基础上，提高了GAP - R BF对单个样本的学习速度。
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