APPLICATION OF HYBRID-LEARNING NEURO-FUZZY GREY-BOX MODELS TO TEMPERATURE PREDICTION IN HOT ROLLING

ALBERTO CAVAZOS GONZALEZ; JOSE ANGEL BARRIOS GOMEZ; GERARDO MAXIMILIANO MENDEZ

doi:https://doi.org/10.6036/7859

PAPER SENDING SUBSCRIPTION

Search engine :

DYNA JOURNAL ENGINEERING

Skip to the menu
Skip to the content

Return to the menu

Search

Vote:

Results:

5 points

1 Votes

APPLICATION OF HYBRID-LEARNING NEURO-FUZZY GREY-BOX MODELS TO TEMPERATURE PREDICTION IN HOT ROLLING

JANUARY 2016 - Volume: 91 - Pages: 16-17

DOI:

https://doi.org/10.6036/7859

Authors:

ALBERTO CAVAZOS GONZALEZ - JOSE ANGEL BARRIOS GOMEZ - GERARDO MAXIMILIANO MENDEZ

Disciplines:

Computer Sciences (ARTIFICIAL INTELLIGENCE / INTELIGENCIA ARTIFICIAL )

Downloads: 174

Cites in Web of Science: 2

How to cite this paper:

Download pdf

Download pdf

Key words:: sistemas difusos tipo-2, modelado de caja gris, aprendizaje híbrido, estimación de temperatura, laminado en caliente, type-2 fuzzy inference systems, grey-box modeling, hybrid learning, temperature estimation, hot rolling
Article type:: NOTA TECNICA / TECHNICAL NOTE
Section:: COLLABORATIONS

Barrios et al. (2015) presented 4 grey-box models based on hybrid-learning type-1 and type-2 fuzzy systems for entry transfer bar temperature prediction in hot rolling. This technical report summarizes the work and presents its main results. Hybrid-learning grey-box models improve significantly the estimation in comparison with previously presented fuzzy systems and the physical model currently used in plant.

Keywords: type-2 fuzzy inference systems, grey-box modeling, hybrid learning, temperature estimation, hot rolling.

Share:

Twittear

Tweets por el @revistadyna.

Anunciarse en DYNA

Address: Unit 7 Wilsons Business Park, Manchester M40 8WN United Kingdom

Email: office@revistadyna.com