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

A Model Predictive Control (MPC) is a system which allows us to control a production plant. Thanks to this type of system is possible to make a production that comes close to “zero defects”. In order to achieve its main goal, this kind of systems consists of several phases. One of the most important is the phase that predicts the situation in which the plant is going to be in a given time. Currently, the majority of the research in this field are related to linear MPC, although the process, which the model tries to represent, may not be. Thus, this paper presents several experiments that proof that the forecast phase, usually represented by a single mathematical function, can be represented by machine-learning models.

Modelo predictivo de control en fundiciones de alta precisión: un nuevo enfoque para la fase de predicción