作者：胡焱, 杨阳, 谢家雨, 李卫青, 蒋陵平

作者单位：中国民用航空飞行学院航空工程学院, 四川 广汉 618307

关键词：极限学习机;航空铅酸蓄电池;容量预测;检测模型

摘要：

针对传统BP神经网络训练速度慢、参数选择难、易陷入局部极值等缺点,提出基于极限学习机(ELM)的航空铅酸蓄电池容量检测模型。极限学习机是一种新的单隐层前馈神经网络(SLFNs)学习算法,不但可以简化参数选择过程,而且可以提高网络的训练速度。在确定最优参数的基础上,建立ELM的航空铅酸蓄电池容量检测模型。实验结果表明:LM获得较高的分类准确率和较快的训练速度,从而验证ELM用于航空铅酸蓄电池容量检测模型的可行性和有效性。

Aviation lead-acid battery capacity detection using extreme learning machine

HU Yan, YANG Yang, XIE Jiayu, LI Weiqing, JIANG Lingping

Aviation Engineering Institute, Civil Aviation Flight University of China, Guanghan 618307, China

Abstract: Aiming at the traditional BP neural network models are inefficient and prone to fall into local extreme values, the extreme learning machine(ELM) was proposed as an alternative in the detection of aviation lead-acid battery capacities. This new learning algorithm for the studies of single-hidden layer feed forward neural networks(SLFNs) can both simplify the parameter selection process and improve the network training speed. The optimal parameters obtained by this algorithm were used to design a model for detecting aviation lead-acid battery capacities. According to the experimental results, the ELM has made classification more accurate and has quickened network training. Thus, it can be used to test the capacity of aviation lead-acid batteries.

Keywords: extreme learning machine;aviation lead-acid battery;capacity detection;detection model

2016, 42(2): 119-121  收稿日期: 2015-2-24;收到修改稿日期: 2015-4-15

基金项目: 中国民用航空飞行学院自然科学面上项目 (XM0514)

作者简介: 胡 焱(1973-),男,四川大英县人,副教授,硕士生导师,主要从事航空电子设备相关研究。

参考文献

[1] 胡恒生. 航空化学电源[D]. 徐州:徐州空军学院,2007.
[2] 欧阳名三,余世杰. VRLA蓄电池容量预测技术的现状及发展[J]. 蓄电池,2004,41(2):59-63.
[3] HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: theory and applications[J]. Neurocomputing,2006,70(1):489-501.
[4] LAN Y, SOH Y C, HUANG G B. Ensemble of online sequential extreme learning machine[J]. Neurocomputing, 2009,72(13-15):3391-3395.
[5] HUANG G B, ZHU Q Y, SIEW C K. Real-time learning capability of neural networks[C]∥IEEE Transact ions on Neural Networks,2006,17(4):863-878.
[6] 史峰,王辉,郁磊,等. Matlab智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011:103-110.
[7] 鲍世静. 铅酸蓄电池剩余容量预测研究和工程应用[D]. 西安:西安电子科技大学,2011.
[8] Modeling and evaluation of valve-regulated lead-acid batteries[R]. Espoo:Helsinki University of Technology Control Engineering Laboratory,2004.
[9] VICTOR L G. Battery charge cycle counter:US0051366 20A[P]. 1992.
[10] 张荣魁.兰州西固热电厂阀控式铅酸蓄电池在线监测及应用研究[D]. 重庆:重庆大学,2004.