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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	504-508
Number of pages	5
Journal	Journal of Scientific and Industrial Research
Volume	78
Issue number	8
Publication status	Published - Aug 2019
Externally published	Yes

Keywords

Forecasting
Fuzzy neural networks
Industrial plants
Predictive models
Time series analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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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.\}",

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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

Abstract

Keywords

UN SDGs

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