文档介绍：电子测量技术
Electronic Measurement Technology
ISSN 1002-7300,CN 11-2175/TN
PSO-LSTM 模型对机械臂的关节变量值进行预测
得到逆运动学解。实验结果表明，模型的逆运动学求解速度维持在 10 ms 以内，与传统解法相比提高了数十倍，且模型的均
方误差低至 ，在提高求解速度的同时还能够保证求解精度。
关键词：机械臂；逆运动学求解；粒子群算法；神经网络
中图分类号：TP242 文献标志码：A 国家标准学科分类代码：
Optimization of LSTM neural network based on PSO
research on inverse kinematics solution of manipulator
Sun Yancheng Chen Fuan
(School of Electrical Engineering，Henan University of Technology，Zhengzhou Henan 450001，China)
Abstract：In order to solve the inverse kinematics of manipulator with poor real-time performance and low precision, a particle
swarm optimization (PSO) algorithm for LSTM was proposed in this paper. Firstly, the model of the series 6-DOF manipulator is
established for kinematic analysis, and the training data are obtained. Next, Optimizing the quantity of hidden level neural units and
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