Adaptive Soft Sensor of Stacking Approximate Kernel- Based Broad Learning System for Batch Process

Abstract

Abstract To deal with the highly nonlinear and time-varying characteristics of batch process, a model named Moving Window Stacking Approximate Kernel-Based Broad Learning System (MW-Stacking-AKBLS) is proposed in this paper. This model innovatively introduces the AKBLS algorithm and the MW-Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The MW-Stacking framework adopts the Stacking ensemble learning method, integrating multiple ABKLS models to enhance the model's generalization ability. Additionally, by adopting the moving window method, the model has been equipped with adaptive ability to better adapt to slow changes in industrial batch process. Finally, comparative experimental results on a substantial dataset of penicillin simulations indicate a significant improvement in predictive accuracy for the proposed MW-Stacking AKBLS model compared to other commonly used algorithms.