Elman neural network for predicting aero optical imaging deviation based on improved slime mould algorithm  

作　　者：XU Liang WANG Luyang XUE Wei ZHAO Shiwei ZHOU Liye 

机构地区：[1]Tianjin Key Laboratory of Complex Control Theory and Application,School of Electronic Engineering and Automation,Tianjin University of Technology,Tianjin 300384,China [2]China Academy of Aerospace Science and Innovation,Beijing 100048,China

出　　处：《Optoelectronics Letters》2023年第5期290-295,共6页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Nos.61975151 and 61308120)。

摘　　要：This research suggests a methodology to optimize Elman neural network based on improved slime mould algorithm(ISMA) to anticipate the aero optical imaging deviation.The improved Tent chaotic sequence is added to the SMA to initialize the population to accelerate the algorithm’s speed of convergence.Additionally,an improved random opposition-based learning was added to further enhance the algorithm’s performance in addressing problems that the SMA has such as weak convergence ability in the late iteration and an easy tendency to fall into local optimization in the optimization process when solving the optimization problem.Finally,the algorithm model is compared to the Elman neural network and the SMA optimization Elman neural network model.The three models are assessed using four evaluation indicators,and the findings demonstrate that the ISMA optimization model can anticipate the aero optical imaging deviation in an accurate way.

关 键 词：NEURAL ELMAN NETWORK 

分 类 号：TJ765[兵器科学与技术—武器系统与运用工程] TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]