Design of the Nonlinear System Predictor Driven by the Bayesian-Gaussian Neural Network of Sliding Window Data


  •  Yijian Liu    
  •  Yanjun Fang    

Abstract

The model identification of the nonlinear system has been concerned by the industrial community all along. The relationship of the nonlinear dynamic system is contained in the data accumulated in the scene. To better utilize the data about the industrial objects, in this article, we put forward the nonlinear system predictor driven by the Bayesian-Gaussian neural network (NN) model, use the trained threshold matrix and sliding window data to realize the online output prediction for the nonlinear dynamic system. The simulation experiment indicates that the Bayesian-Gaussian NN based on the sliding window data can fulfill the demands of the online identification and prediction of the adaptive nonlinear system.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

WJCI (2020): 0.439

Impact Factor 2020 (by WJCI): 0.247

Google Scholar Citations (March 2022): 6907

Google-based Impact Factor (2021): 0.68

h-index (December 2021): 37

i10-index (December 2021): 172

(Click Here to Learn More)

Contact