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 XU Liang;WANG Luyang;XUE Wei;ZHAO Shiwei;ZHOU Liye

作者机构：Tianjin Key Laboratory of Complex Control Theory and ApplicationSchool of Electronic Engineering and AutomationTianjin University of TechnologyTianjin 300384China China Academy of Aerospace Science and InnovationBeijing 100048China 

出 版 物：《Optoelectronics Letters》 (光电子快报（英文版）)

年 卷 期：2023年第19卷第5期

页      面：290-295页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081105[工学-导航、制导与控制] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：neural Elman network 

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 *** improved Tent chaotic sequence is added to the SMA to initialize the population to accelerate the algorithm’s speed of ***,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 ***,the algorithm model is compared to the Elman neural network and the SMA optimization Elman neural network *** 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.