English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The Industrial Internet of Things (IIoT) is of strategic importance in the new era of industrial big data, creating a brand-new industrial ecosystem. Considering the unknown parameters in the IIoT-based industrial process control systems, this paper combines the artificial fish swarm algorithm (AFSA) and the particle filtering (PF) algorithm into the AFSA-PF algorithm based on the self-organizing state space (SOSS) model. The AFSA-PF algorithm not only can estimates the system state but also can make the sampling distribution of the unknown parameter to move the true parameter distribution. Ultimately, the true values of the unknown parameters are identified. In this way, the system model can gradually approximate the actual IIoT-based industrial process control system.

mathematical problems in engineering

Application of the Artificial Fish School Algorithm and Particle Filter Algorithm in the Industrial Process Control Particle Filtering Algorithm for Industrial Process Control