Application of quantum neural networks in localization of acoustic emission

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.