An online intelligent detection method for slurry density in concept drift data streams based on collaborative computing.

ABSTRACT: In industrial environments, slurry density detection models often suffer from performance degradation due to concept drift. To address this, this article proposes an intelligent detection method tailored for slurry density in concept drift data streams. The method begins by building a model using Gaussian process regression (GPR) combined with regularized stochastic configuration. A sliding window-based online GPR is then applied to update the linear model's parameters, while a forgetting mechanism enables online recursive updates for the nonlinear model. Network pruning and stochastic configuration techniques dynamically adjust the nonlinear model's structure. These approaches enhance the mechanistic model's ability to capture dynamic relationships and reduce the data-driven model's relia

SUBMITTER: Wang L 

PROVIDER: S-EPMC11888939 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature