Genetic-based on-line learning for fuzzy process control
Juan R. Velasco · International Journal of Intelligent Systems · 1998
This paper deals with the problem of continuous learning in process control. Conventional machine learning applied to process control tries to obtain control rules from an historic data file or a model. However, these learned rules may be useless if the real process changes, and this is not unusual. To try to solve this problem, genetic algorithms can be used in a continuous learning environment. However, genetically generated rules do not guarantee that they are good enough to control the process. New rules should be tested before their insertion into the knowledge base: this is the function of Limbo. Limbo is a special place where rules can be tested in real situations before being used. This paper shows how Limbo can be used to improve continuous learning. © 1998 John Wiley & Sons, Inc.