Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining and Knowledge Discovery for Process Monitoring and Control
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Fuzzy Model Identification for Control
Fuzzy Model Identification for Control
Internet-based remote supervision of industrial processes using self-organizing maps
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Application of SOM-based visualization maps for time-response analysis of industrial processes
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Analysis of flue gas emission data from fluidized bed combustion using self-organizing maps
Applied Computational Intelligence and Soft Computing
Expert system for analysis of quality in production of electronics
Expert Systems with Applications: An International Journal
Self-organizing maps of nutrition, lifestyle and health situation in the world
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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The huge amount of data recorded by modern production systems definitely have the potential to provide information for product and process design, monitoring and control. This paper presents a soft-computing (SC)-based approach for the extraction of knowledge from the historical data of production. Since Self-Organizing Maps (SOM) provide compact representation of the data distribution, efficient process monitoring can be performed in the two-dimensional projection of the process variables. For the estimation of the product quality, multiple local linear models are identified, where the operating regimes of the local models are obtained by the Voronoi diagram of the prototype vectors of the SOM. The proposed approach is applied to the analysis of an industrial polyethylene plant. The detailed application study demonstrates that the SOM is very effective in the detection of the typical operating regions related to different product grades, and the model can be used to predict the product quality (melt index and density) based on measured process variables.