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Related Experiment Video

Updated: Jan 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

362

Robust self-organizing fuzzy neural network with data immunity evaluation for industrial process modeling.

Zheng Liu1, Guoqing Cai1, Honggui Han1

  • 1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Engineering Research Center of Digital Community Ministry of Education, Beijing University of Technology, Beijing 100124, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 24, 2025
PubMed
Summary

A novel robust self-organizing fuzzy neural network with data immunity evaluation (RSOFNN-DIE) enhances industrial process modeling. This approach improves accuracy and robustness by effectively handling noisy and uncertain data.

Keywords:
Convergence and robustnessData immunity evaluation strategyEvaluation penalty mechanismModelRobust self-organizing fuzzy neural network

Related Experiment Videos

Last Updated: Jan 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

362

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Control Systems

Background:

  • Neural networks offer dynamic adaptability but struggle with industrial data uncertainty, outliers, and noise.
  • Existing models face limitations in promoting accuracy and robustness due to data quality issues in industrial applications.

Purpose of the Study:

  • Develop a robust self-organizing fuzzy neural network with data immunity evaluation (RSOFNN-DIE) for industrial process modeling.
  • Enhance model robustness and accuracy by addressing data uncertainty, outliers, and noise.

Main Methods:

  • Implemented a data immunity evaluation strategy to determine network structure and enhance robustness.
  • Introduced a parameter learning algorithm with an evaluation penalty mechanism to adapt model parameters to changing input data.
  • Analyzed the convergence and robustness of the RSOFNN-DIE model theoretically.

Main Results:

  • The RSOFNN-DIE demonstrated superior model performance in industrial process modeling.
  • The proposed model effectively alleviated sensitivity to outliers and noise, improving anti-interference capabilities.
  • Theoretical validation confirmed the effective implementation of RSOFNN-DIE in noisy environments.

Conclusions:

  • RSOFNN-DIE provides a robust and accurate solution for industrial process modeling, even with uncertain data.
  • The developed data immunity evaluation and parameter learning strategies significantly enhance model performance.
  • The findings support the practical application of RSOFNN-DIE in challenging industrial environments.