Genetic algorithm-based stochastic distribution control for non-Gaussian systems
Author:
Clc Number:

TP13

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

    Traditional control theory does not consider some uncertain factors in the industrial process,which has a significant impact on energy loss and system accuracy in production process.This paper considers the data-based optimization control strategies for non-Gaussian stochastic systems.Kernel density estimation was used to estimate the output probability density functions (PDFs) on the basis of collected output data.Firstly,the performance index function was established based on the control objectives.Secondly,the performance index function was optimized by a genetic algorithm.The simulation takes the grinding system as a model and uses PDFs to characterize the particle size distribution.Simulation results show that the genetic algorithm-based stochastic distribution control for non-Gaussian systems can effectively achieve the control target of the stochastically distributed control system and provides reference for practical industrial production.

    Reference
    Related
    Cited by
Get Citation

HONG Yue, YIN Liping. Genetic algorithm-based stochastic distribution control for non-Gaussian systems[J]. Journal of Nanjing University of Information Science & Technology,2020,12(4):504-509

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:November 21,2018
  • Online: July 31,2020
Article QR Code

Address:No. 219, Ningliu Road, Nanjing, Jiangsu Province

Postcode:210044

Phone:025-58731025