采用基于基本解方法和径向基函数插值的无网格算法(MFS - RBF)分析了广义的热弹性问题。
The method of fundamental solutions (MFS) with radial basis functions (RBF) approximation was developed for general thermoelastic analysis.
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。
A two-level learning method combining improved immune algorithm and least square method was proposed to design a radial basis function (RBF) network.
在RBF网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，使网络结构得到优化。
A learning algorithm of subtractive clustering for RBF network is used to obtain the parameters of radial basis function so as to optimize network structure.
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