Industrial process condition forecasting methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps

D. Zurita; M. Delgado-Prieto; J. A. Cariño; G. Clerc; J. A. Ortega; H. Razik; R. A. Osornio-Rios

Industrial process condition forecasting methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps

D. Zurita
, M. Delgado-Prieto
, J. A. Cariño
, G. Clerc
, J. A. Ortega
, H. Razik
, R. A. Osornio-Rios^*

^*Autor correspondiente de este trabajo

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

1 Cita (Scopus)

Resumen

The condition forecasting of industrial processes represents a key factor to allow the future generation of industrial manufacturing plants. In this regard, this paper presents a novel soft-computing based methodology for the assessment of the current and future condition of industrial processes by the combination of Neo Fuzzy Neuron (NFN) and Self-Organizing Maps (SOM) data-driven based modelling. The proposed method models, individually, the critical signals describing the industrial process.

Idioma original	Inglés
Páginas (desde-hasta)	504-508
Número de páginas	5
Publicación	Journal of Scientific and Industrial Research
Volumen	78
N.º	8
Estado	Publicada - ago 2019
Publicado de forma externa	Sí

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 9: Industria, innovación e infraestructura

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

@article{589f2da1b9c84e439ff4f1042800ff5f,

title = "Industrial process condition forecasting methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps",

abstract = "The condition forecasting of industrial processes represents a key factor to allow the future generation of industrial manufacturing plants. In this regard, this paper presents a novel soft-computing based methodology for the assessment of the current and future condition of industrial processes by the combination of Neo Fuzzy Neuron (NFN) and Self-Organizing Maps (SOM) data-driven based modelling. The proposed method models, individually, the critical signals describing the industrial process.",

keywords = "Forecasting, Fuzzy neural networks, Industrial plants, Predictive models, Time series analysis",

author = "D. Zurita and M. Delgado-Prieto and Cari{\~n}o, \{J. A.\} and G. Clerc and Ortega, \{J. A.\} and H. Razik and Osornio-Rios, \{R. A.\}",

year = "2019",

month = aug,

language = "English",

volume = "78",

pages = "504--508",

journal = "Journal of Scientific and Industrial Research",

issn = "0022-4456",

publisher = "National Institute of Science Communication and Information Resources (NISCAIR)",

number = "8",

}

TY - JOUR

T1 - Industrial process condition forecasting methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps

AU - Zurita, D.

AU - Delgado-Prieto, M.

AU - Cariño, J. A.

AU - Clerc, G.

AU - Ortega, J. A.

AU - Razik, H.

AU - Osornio-Rios, R. A.

PY - 2019/8

Y1 - 2019/8

N2 - The condition forecasting of industrial processes represents a key factor to allow the future generation of industrial manufacturing plants. In this regard, this paper presents a novel soft-computing based methodology for the assessment of the current and future condition of industrial processes by the combination of Neo Fuzzy Neuron (NFN) and Self-Organizing Maps (SOM) data-driven based modelling. The proposed method models, individually, the critical signals describing the industrial process.

AB - The condition forecasting of industrial processes represents a key factor to allow the future generation of industrial manufacturing plants. In this regard, this paper presents a novel soft-computing based methodology for the assessment of the current and future condition of industrial processes by the combination of Neo Fuzzy Neuron (NFN) and Self-Organizing Maps (SOM) data-driven based modelling. The proposed method models, individually, the critical signals describing the industrial process.

KW - Forecasting

KW - Fuzzy neural networks

KW - Industrial plants

KW - Predictive models

KW - Time series analysis

UR - https://www.scopus.com/pages/publications/85104271784

M3 - Article

AN - SCOPUS:85104271784

SN - 0022-4456

VL - 78

SP - 504

EP - 508

JO - Journal of Scientific and Industrial Research

JF - Journal of Scientific and Industrial Research

IS - 8

ER -

Industrial process condition forecasting methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps

Resumen

ODS de las Naciones Unidas

Otros archivos y enlaces

Huella

Citar esto