飞行器智能蒙皮通过在飞行器复合材料蒙皮上集成分布式传感器、驱动器和控制器,使其具有监测其本身状态和损伤的能力,其中物理场反演算法是智能蒙皮信号处理中的重要一环。但是由于传感器布置密度小等原因,传统的反演算法精度不高。为...飞行器智能蒙皮通过在飞行器复合材料蒙皮上集成分布式传感器、驱动器和控制器,使其具有监测其本身状态和损伤的能力,其中物理场反演算法是智能蒙皮信号处理中的重要一环。但是由于传感器布置密度小等原因,传统的反演算法精度不高。为了提高智能蒙皮的监测精度,提出一种将反向传播(back propagation,BP)神经网络与改进的灰狼优化算法(improved grey wolf optimizer,IGWO)优化克里金模型融合的BP-IGWO反演算法。制作智能蒙皮原理样件,通过风洞试验对该算法进行验证。结果表明:BP-IGWO反演算法较之传统反演算法具有更高的精度和细节呈现能力,能更好地监测智能蒙皮的状态。展开更多
Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to ca...Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation（BP） neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.展开更多
文摘飞行器智能蒙皮通过在飞行器复合材料蒙皮上集成分布式传感器、驱动器和控制器,使其具有监测其本身状态和损伤的能力,其中物理场反演算法是智能蒙皮信号处理中的重要一环。但是由于传感器布置密度小等原因,传统的反演算法精度不高。为了提高智能蒙皮的监测精度,提出一种将反向传播(back propagation,BP)神经网络与改进的灰狼优化算法(improved grey wolf optimizer,IGWO)优化克里金模型融合的BP-IGWO反演算法。制作智能蒙皮原理样件,通过风洞试验对该算法进行验证。结果表明:BP-IGWO反演算法较之传统反演算法具有更高的精度和细节呈现能力,能更好地监测智能蒙皮的状态。
基金supported by the National Natural Science Foundation of China (51075068)the Southeast University Science Foundation Funded Program (KJ2009348)
文摘Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation（BP） neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.
