Sparse Broad Learning System via Sequential Threshold Least-Squares for Nonlinear System Identification under Noise

TL;DR

This paper introduces a Sparse Broad Learning System (S-BLS) that uses Sequential Threshold Least-Squares to improve robustness against noise in nonlinear system identification, outperforming standard BLS in noisy environments.

Contribution

It integrates STLS into BLS to promote sparsity and robustness, addressing noise sensitivity issues of traditional BLS methods.

Findings

S-BLS achieves higher accuracy in noisy nonlinear system identification.

Experimental results show improved robustness over standard BLS.

Effective filtering of noise components while maintaining model performance.

Abstract

The Broad Learning System (BLS) has gained significant attention for its computational efficiency and less network parameters compared to deep learning structures. However, the standard BLS relies on the pseudoinverse solution, which minimizes the mean square error with $L_2$-norm but lacks robustness against sensor noise and outliers common in industrial environments. To address this limitation, this paper proposes a novel Sparse Broad Learning System (S-BLS) framework. Instead of the traditional ridge regression, we incorporate the Sequential Threshold Least-Squares (STLS) algorithm -- originally utilized in the sparse identification of nonlinear dynamics (SINDy) -- into the output weight learning process of BLS. By iteratively thresholding small coefficients, the proposed method promotes sparsity in the output weights, effectively filtering out noise components while maintaining modeling accuracy. This approach falls under the category of sparse regression and is particularly suitable for noisy environments. Experimental results on a numerical nonlinear system and a noisy Continuous Stirred Tank Reactor (CSTR) benchmark demonstrate that the proposed method is effective and achieves superior robustness compared to standard BLS.