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.