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/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The paper involved conducting preliminary research to explore the identification and control of a multi-dimensional, non-linear, and non-stationary cement grinding process using artificial neural networks and various other non-linear models. The primary objective was to establish a precise model that accurately characterizes the functioning of the grinding system. Several model structures were employed, including NARX models based on feed-forward network, Elman, Jordan, and Layer-Recurrent Network (LRN) recurrent networks, as well as MTL (Multi-Task Learning) and traditional NARX non-linear models. It was observed that, in contrast to the linear models, the non-linear models exhibited significantly superior performance in the modeling of the system. Another notable outcome of this research is the proposal of a neurocontroller, functioning as an expert system, which can provide control signals to operators. The development and implementation of such a neurocontroller have the potential to enhance the quality, simplicity, and efficiency of cement grinding process control.

Identification and expert approach to controlling the cement grinding process using artificial neural networks and other non-linear models