Research on improved grasshopper optimization algorithm in PID control of fuzzy neural networks
DOI:
CSTR:
Author:
Affiliation:

College of Automation and Electronic Enginnering,Qingdao University of Science and Technology,Qingdao 266061,China

Clc Number:

TP273

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    The traditional fuzzy neural PID control algorithm is prone to the problem of poor control effect caused by improper adjustment of network parameters. This paper presents a fuzzy neural network PID control algorithm optimized by improved grasshopper algorithm. Aimed at the problem of insufficient traditional algorithms of grasshopper particle diversity firstly introduced Levy random flight strategy, secondly introduce nonlinear reduction factor and simulated annealing algorithm to improve the optimization ability and the ability to jump out of local optimal solution, then the improved grasshopper algorithm combined with fuzzy neural PID neural network is optimized by super parameter and control parameter self-tuning, Finally, the simulation results verify the superiority and reliability of the proposed improved grasshopper algorithm to optimize the fuzzy neural network PID algorithm.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:
  • Revised:
  • Adopted:
  • Online: March 27,2024
  • Published:
Article QR Code