摘要
针对飞行数据的特点,提出了一种基于动态模糊神经网络(DFNN)的飞行数据模型辨识方法。该方法采用在线学习方式,通过动态增加和删除神经元节点的策略实现网络结构学习,采用递推最小二乘法实现网络权值的在线调整,以最终得到一个结构简单、泛化能力强的神经网络。以某特定时间段的飞参数据为仿真样本,将该DFNN用于参数关联模型的辨识,实验结果表明该辨识方法收敛速度快、泛化能力强。
With regard to the characteristics of flight data, a new identification method of flight data model based on dynamic fuzzy neural network is presented. By on - line learning, the proposed DFNN is learned for a compact network with better generalization ability. The network structure is learned by means of adding or pruning a new neuron, furthermore, the linear parameters as network weights are gained based on the recursive least squares algorithm. Through a great number of observations in a certain sortie, the DFNN method is applied to the identification of the association model of flight data. The test results show that the method is of faster constringency and better generalization.
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2006年第6期16-18,共3页
Journal of Air Force Engineering University(Natural Science Edition)
基金
军队科研基金资助项目
关键词
动态模糊神经网络
飞行数据
模糊规则
辨识
dynamic fuzzy neural network
flight data
fuzzy rule
identification
