Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/49561
Title: Industrial Process Condition Forecasting Methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps
Authors: Zurita, D
Delgado-Prieto, M
Cariño, J A
Clerc, G
Ortega, J A
Razik, H
Osornio-Rios, R A
Keywords: Forecasting;Fuzzy neural networks;Industrial plants;Predictive models;Time series analysis
Issue Date: Aug-2019
Publisher: NISCAIR-CSIR, India
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.
Page(s): 504-508
URI: http://nopr.niscair.res.in/handle/123456789/49561
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.78(08) [August 2019]

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