摘要
本文首先详细地阐述了Elman神经网络的结构、原理和学习算法.为了进一步提高Elman神经网络的逼近能力和动态特性,我们提出了一种改进的Elman神经网络模型.这种新的Elman神经网络在关联节点与输出节点之间又增加了一组可调权值,利用误差回馈原理推导出了其相应的学习算法.仿真实验结果表明,改进的Elman神经网络比原来的网络具有更好的动态性能,对于贯序输入输出数据的逼近收敛速度更快.
This paper first discusses the structure, principle and learning algorithm of Elman neural network model. A modified Ehnan neural network model is then proposed by adding new adjustable weights between the context nodes and the output nodes to enhance its dynamical character. The corresponding learning algorithm is also derived by using steepest descent principle. Theoretical analysis and simulation results show that this kind of modified Ehnan neural network learns much faster than the original model.
基金
航天基础研究基金
关键词
ELMAN
神经网络
学习算法
Elman neural network, Approximation, Learning algorithm
