摘要
从径向基函数（RBF）神经网络原理分析出发，提出了一种基于RBF神经网络学习算法，用于对非线性对象模型的拟合与辨识，并将此方法用于实际非线性模型的学习与辨识。结果表明，基于RBF的神经网络可快速完成对样本的学习与拟合，对具有连续特性的线性与非线性模型，具有快速实时的学习速度和优良的学习性能。
According to the mechanism of radial base function(RBF) neural network, this paper presents a method. based on RBF neural network for the recognition of nonlinear system model. The RBF algorithm is applied to the learning and recognizing process of the nonlinear model. The simulations show the presented mathod has good effects on speeding up the learning and approaching process of the nonlinear model, and has an excellent performance on learning convergence.
出处
《计算机工程》
CAS
CSCD
北大核心
2004年第5期142-143,169,共3页
Computer Engineering
基金
山东省教育厅科技计划项目(J02F01)
