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

TL;DR

This paper introduces a new machine learning system for industrial applications that handles complex data efficiently and accurately.

Contribution

The novel CI-GBLS system uses channel independence and graph regularization to improve efficiency and accuracy in industrial modeling.

Findings

CI-GBLS achieves high prediction accuracy and efficiency on complex industrial data.

The model completes training within seconds, suitable for real-time applications.

It effectively captures nonlinear dynamics and spatial coupling relationships.

Abstract

To address the nonlinear dynamics and strong multivariate coupling inherent in complex industrial data, while overcoming the high computational costs and deployment challenges of deep learning, this paper proposes a Channel-Independent Anchor Graph-Regularized Broad Learning System (CI-GBLS). First, a Channel Independence (CI) strategy is introduced: by constructing physically isolated feature channels, multivariate inputs are orthogonally decomposed, enabling the model to mine the intrinsic temporal evolutionary patterns of each variable. Building upon this, enhancement nodes are constructed using Radial Basis Functions (RBFs) to capture nonlinear dynamics; moreover, RBF cluster centers are reused as graph anchors to design an efficient manifold regularization algorithm. This algorithm embeds the intrinsic geometric structure of the data into the learning objective via reduced rank approximation, thereby guiding output weights to explicitly reconstruct spatial coupling relationships while preserving manifold consistency. Experimental results on the IndPenSim process demonstrate that CI-GBLS effectively balances prediction accuracy and efficiency. It completes training within seconds, validating its effectiveness for complex time-series data and offering an efficient solution for real-time, high-precision industrial modeling.

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