机构

• 长春光学精密机械与物... [1]
• 自动化研究所 [1]
• 沈阳自动化研究所 [1]
• 广州能源研究所 [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2020 [1]
• 2017 [1]
• 2016 [1]
• 2011 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution 期刊论文  OAI收割 International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 6, 页码: 867-873 作者:  Hong-Tao Ye;  Zhen-Qiang Li   |  收藏  |  浏览/下载：40/0  |  提交时间：2021/02/22 Particle swarm optimization  differential evolution  proportion integration differentiation (PID) neural network  hybrid approach  decoupling control.   基于BP神经网络整定的PID控制及其仿真 期刊论文  OAI收割 山东陶瓷, 2017, 卷号: 40, 期号: 3, 页码: 27-31 作者:  高富强;  李萍;  张磊敏;  曾令可;  涂腾   |  收藏  |  浏览/下载：25/0  |  提交时间：2018/12/21 PID  整定  BP神经网络  仿真  PID  Tuning  Back Propagation Neural Network  Simulation   Attitude control for astronaut assisted robot in the space station 期刊论文  OAI收割 International Journal of Control, Automation and Systems, 2016, 卷号: 14, 期号: 4, 页码: 1082-1095 作者:  Liu JG(刘金国);  Gao Q(高庆);  Liu ZW(刘志伟);  Li YM(李杨民) 收藏  |  浏览/下载：30/0  |  提交时间：2016/07/22 Astronaut assisted robot  attitude control  PID Neural Network  space station   Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文  OAI收割 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China 作者:  Fan Y. 收藏  |  浏览/下载：47/0  |  提交时间：2013/03/25 To deal with the defects of BP neural networks used in balance control of inverted pendulum  such as longer train time and converging in partial minimum  this article reaLizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks(ANN)  builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and PureLin function is used in output layer  LM is used in training algorithm. The training data is acquried by three-loop PID algorithm. The model is learned and trained with Matlab calculating software  and the simuLink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision  better astringency and lower calculation. This algorithm has wide appLication on nonLinear control and robust control field in particular. 2011 IEEE.