摘要
为了提高E lm an网络的动态性能,有效地解决高阶系统的辨识问题,对E lm an网络的结构及学习算法进行了改进,提出了一种新的E lm an网络,建立了相应的神经网络载荷识别模型,并用于齿轮箱的载荷识别研究。试验结果表明,该网络模型具有收敛速度快、识别精度高的特点,为载荷识别研究提供了一种新的思路,具有一定的实用价值。
To enhance the dynamic capability of the Elman network and to solve effectively the problem in identifying high order system, a new Elman network is developed by improving the network structure and learning arithmetic. Corresponding neural network load identification model was established, which was used to identify the load of a gearbox. The Experiment results showed that the improved neural network model has the characteristics of an extremely fast convergence and a relatively high accuracy. The investigation made in this paper provides a new approach for load identification research and is of certain practical usefulness.
出处
《振动工程学报》
EI
CSCD
北大核心
2006年第1期114-117,共4页
Journal of Vibration Engineering
基金
国家自然科学基金资助(59975087)
