本文使用径向基神经网络(RBF neural network)来对 且_(∞)进行插值处理,在保证较高插值精度的前提下, 显著地提高了模拟计算效率,从而节约了模拟计算的 时间。
基于12个网页-相关网页
后级使用径向基神经网络作信号拟合,提取SEP信号的特征。
Afterward, its output is estimated by radial basis function neural network (RBFNN) for extracting SEP features.
本文使用径向基神经网络确定各个因素之间的非线性复杂关系。
In this paper, radial basis function neural network was employed to approximate the nonlinear complex relationship of all factors.
文中使用径向基函数理论建立了基于RBF神经网络的数控机床热误差数学模型。
A neural network based on radial basis function (RBF) was used to predict and compensate the thermal error of a CNC turning center.
应用推荐