Outlier Detection and Data Cleaning in Multivariate Non-Normal Samples: The PAELLA Algorithm
Data Mining and Knowledge Discovery
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A neural network-based approach for optimising rubber extrusion lines
International Journal of Computer Integrated Manufacturing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Trends extraction and analysis for complex system monitoring and decision support
Engineering Applications of Artificial Intelligence
Preventive maintenance scheduling for repairable system with deterioration
Journal of Intelligent Manufacturing
A novel agent-based concept of household appliances
Journal of Intelligent Manufacturing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Coordinating metaheuristic agents with swarm intelligence
Journal of Intelligent Manufacturing
Modelling and simulation of dynamically integrated manufacturing systems
Journal of Intelligent Manufacturing
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On-line manufacturing systems for continuous production are traditionally checked by constant time-based inspection and statistical process control processes. In the monitoring operation of large scale process plants, it is important to detect and locate process variables as they can significantly modify the operation and quality of the products involved. The present paper explores an alternative for monitoring these systems that uses the open loop control paradigm. The proposed method is defined and presented as an application for one particular industrial process--rubber die extrusion. The advantages of this method for this particular application, including its implementation throughout a multi-agent system, compatible with the HoloMAS paradigm for managing, modeling and supporting complex systems, are also discussed.