Fast Sensitivity-Analysis-Based Online Self-Organizing Broad Learning System

Modern industrial process modeling requires models to adapt quickly to real-time operating conditions. To this end, this article proposes a fast sensitivity analysis (SA)-based self-organizing broad learning system (SASO-BLS) that offers a paradigm for theory-guided online structural self-adaptation of differentiable models. Specifically, SASO-BLS is implemented by embedding in BLS an interpretable and efficient model compression method called fast partial differential-based SA (FPD-SA). Unlike conventional SA methods that require iterative evaluation of SA indexes for all samples, FPD-SA exploits the deduced chain rule across categories, effectively mitigating the computational burden imposed by industrial Big Data and reducing computational time. In addition, we derive the offline SASO-BLS algorithm for discrete data and extend it to the online scenario for real-time streaming data processing. Note that both modes obviate the necessity of recalculating the pseudo-inverse of the entire state matrix, facilitating SASO-BLS in attaining remarkable efficiency in structural self-organization. Finally, a theoretical justification of the universal approximation property for SASO-BLS is presented. Experimental results on a fault diagnosis benchmark dataset and a real industrial process one demonstrate the effectiveness of the proposed approach.