Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction

IEEE Access

Zhenhua Yu¹  Ziye Zhang²  Liang Xu²  Yuan Yao² 

[1]Institute of Systems Security and Control, College of Computer Science and Technology, Xi&x2019

[2]Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China

关键词: Back propagation neural networks;    imaging deviation;    improving particle swarm optimization algorithm;    prediction;   

DOI  :  10.1109/ACCESS.2021.3102669

来源: DOAJ

【 摘 要 】

In this article, an improved particle swarm optimization (IPSO) algorithm based on similarity and random mutation is raised. The diversity of particles in the population is decided by the size of the aggregation. When the aggregation degree of particles in the population surpass a certain threshold, the concept of similarity is used to measure the similarity between particles and global extremum, and the particles with higher similarity are discretized by mutation strategy. By increasing the particle swarm’s diversity, the population’s local and global search ability tend to balance. The weight and threshold of the back propagation (BP) neural networks are optimized by the IPSO algorithm. Then, the model of the improved particle swarm optimization back propagation neural network (IPSO-BP) is applied to the aero-optical imaging deviation prediction. The results show that the prediction accuracy of the IPSO-BP model is superior to the PSO-BP model, the extreme learning machine (ELM) model, and the least square support vector machine (LSSVM) model, and its convergence speed is faster than that of the PSO-BP neural network model. Finally, the application of deep learning in aero-optical imaging deviation prediction is analyzed compared with the IPSO-BP neural network model.

【 授权许可】

Unknown