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A Channel-Independent Anchor Graph-Regularized Broad Learning System for Industrial Soft Sensors.

Zhiyi Zhang1,2, Mingyi Yang1,2, Cheng Xie1

  • 1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.

Entropy (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new Channel-Independent Anchor Graph-Regularized Broad Learning System (CI-GBLS) for complex industrial data. CI-GBLS efficiently models nonlinear dynamics and multivariate coupling, offering high accuracy and speed for time-series analysis.

Keywords:
anchor graphbroad learning systemchannel independencemanifold regularizationquality predictionsoft sensor

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Complex industrial data exhibit nonlinear dynamics and strong multivariate coupling.
  • Deep learning methods face high computational costs and deployment challenges for such data.

Purpose of the Study:

  • Propose a novel Channel-Independent Anchor Graph-Regularized Broad Learning System (CI-GBLS).
  • Overcome computational and deployment challenges of deep learning in industrial modeling.

Main Methods:

  • Introduce a Channel Independence (CI) strategy for orthogonal decomposition of multivariate inputs.
  • Utilize Radial Basis Functions (RBFs) for enhancement nodes to capture nonlinear dynamics.
  • Employ RBF cluster centers as graph anchors for manifold regularization, preserving geometric structure via reduced rank approximation.

Main Results:

  • CI-GBLS effectively balances prediction accuracy and computational efficiency.
  • The model completes training within seconds on complex time-series data.
  • Demonstrated effectiveness on the IndPenSim process for industrial modeling.

Conclusions:

  • CI-GBLS provides an efficient solution for real-time, high-precision industrial modeling.
  • The approach successfully mines intrinsic temporal patterns and reconstructs spatial coupling relationships.
  • CI-GBLS offers a viable alternative to deep learning for complex industrial data analysis.