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 lack of proper analytical models of some production processes prevents us from obtaining proper values of process parameters by simply computing optimal values. Possible solutions of control problems in such areas of industrial processes can be found using certain methods from the domain of artificial intelligence: neural networks, fuzzy logic, expert systems, or evolutionary algorithms. Presented in this work, a solution to such a control problem is an alternative approach that combines control of the industrial process with learning based on production results. By formulating the main assumptions of the proposed methodology, decision processes of a human operator using his experience are taken into consideration. The researched model of using and gathering experience of human beings is designed with the contribution of agent technology. The presented solution of the control problem coincides with case-based reasoning (CBR) methodology

Agents modelling experience applied to control of semi-continuous production process