摘要
文献[4]提出一种训练多层前向神经网络的快速学习算法—RLS算法,与标准BP算法相比有较高的学习效率,但该方法的主要缺陷是存在数值稳定问题和鲁棒性不强的问题。提出了一种修正的基于递推最小二乘算法(RLS)的多层前向神经网络的快速学习算法,证明了算法的数值稳定性,对两个系统进行了辨识,并与RLS训练算法和标准BP算法进行了比较,仿真结果显示了所提方法的鲁棒性和有效性。
Paper [4] proposes a quick learning algorithm —the recursive least square (RLS), a m ethod for the m ultilayerfeedforward neuralnetw orks. Com pared with standard BPalgorithm , this algorithm has a high learning efficiency, w hile it's stablization and robustness are not high. This paper proposes a m odified learning algorithm based on theRLSm ethod, and dem onstratesthe m odified algorithm 'sstablization.The algorithm hasbeen applied to the identifications of two system s. The sim ulation results dem onstrate the robustness of the system and the effectivenessofthe proposed m ethod.
出处
《系统工程与电子技术》
EI
CSCD
2000年第1期77-80,共4页
Systems Engineering and Electronics
关键词
RLS算法
神经网络
学习算法
Nervoussystem Network structure Leastsquarem ethod Sim ulation