在数学建模领域，径向基函数网络（Radial basis function network，缩写 RBF network）是一种使用径向基函数作为激活函数的人工神经网络。径向基函数网络的输出是输入的径向基函数和神经元参数的线性组合。径向基函数网络具有多种用途，包括包括函数近似法、时间串行预测、分类和系统控制。他们最早由布鲁姆赫德（Broomhead）和洛维（Lowe）在1988年创建。

径向基函数网络通常有三层：输入层、隐藏层和一个非线性激活函数和线性径向基神经网络输出层。输入可以被建模为实数矢量。输出是输入矢量的一个标量函数。

In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control. They were first formulated in a 1988 paper by Broomhead and Lowe, both researchers at the Royal Signals and Radar Establishment.