摘要
针对用复杂飞行数据识别飞行载荷时的精度低、速度慢等问题,提出一种结合遗传算法(GA)和极限学习机(ELM)的GA-ELM模型。该模型使用ELM神经网络作为计算核心,用遗传算法产生ELM网络输入层到隐含层的权值矩阵和隐含层偏移量;用GA-ELM模型对飞行数据进行识别,并与BP神经网络和原始ELM神经网络的识别结果进行对比。结果表明:GA-ELM模型是一种有效且高精度的飞行载荷参数识别方法。
A parametric flight loads identification method suitable for complex flight data is presented,which combines genetic algorithm（GA）and extreme learning machine（ELM）.The model is based on ELM method,and GA is used to develop bias weight and weight matrix between input and hidden layer in ELM network.In the end,GA-ELM model is used to identify flying load based on real flying data.The identify result is compared with that of BP network and original ELM method,and GA-ELM model is proved to be validated,accuracy and feasible.
出处
《航空工程进展》
2014年第4期497-501,共5页
Advances in Aeronautical Science and Engineering
基金
国家自然科学基金(11102158)
