Research and Improvement of PID Neural Network

LU  Ping; JIN  Chao-Yong

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Research and Improvement of PID Neural Network

更新时间：2026-05-27

- Research and Improvement of PID Neural Network
- Vol. 28, Issue 4, Pages: 55-58(2011)
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- Online First：25 December 2011，
  
  Published：2011
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卢萍, 金朝永. 收敛速度；误差迭代；神经网络；仿真； PID控制[J]. 广东工业大学学报, 2011, 28(4): 55-58.

LU Ping, JIN Chao-Yong. Research and Improvement of PID Neural Network[J]. 2011, 28(4): 55-58.
卢萍, 金朝永. 收敛速度；误差迭代；神经网络；仿真； PID控制[J]. 广东工业大学学报, 2011, 28(4): 55-58. DOI：

LU Ping, JIN Chao-Yong. Research and Improvement of PID Neural Network[J]. 2011, 28(4): 55-58. DOI：

摘要

针对PID神经网络能根据控制效果进行在线自学习调整及具有无静差控制效果等特点，通过分析PIDNN控制算法存在的局限性，提出了对误差进行分级的改进算法.对改进的算法进行仿真，结果证明该方法能有效地改善网络的性能.

Abstract

Considering that the neural network of Proportion Integration Differentiation (PID）can adjust online self-learning and has no static errors

it proposed an improved algorithm for classification of errors by analyzing the defects of the control method. Simulation was done for this improved algorithm. The results indicate that the improved algorithm can improve the performance of the network effectively.

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references

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李文峰,刘军营,盛蕾.一种改进的基于神经网络模式的PID控制研究[J].系统仿真技术,2007,3(4): 217-220.

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