Industrial Process Condition Forecasting Methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Scientific and Industrial Research Année : 2019

Industrial Process Condition Forecasting Methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps

Résumé

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.
Fichier non déposé

Dates et versions

hal-03683021 , version 1 (31-05-2022)

Identifiants

  • HAL Id : hal-03683021 , version 1

Citer

Daniel Zurita, Miguel Delgado Prieto, J.A. Carino, Guy Clerc, Ortega J.A., et al.. Industrial Process Condition Forecasting Methodology based on Neo-Fuzzy Neuron and Self-Organizing Maps. Journal of Scientific and Industrial Research, 2019, 78, pp.504-508. ⟨hal-03683021⟩
33 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More