Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Multi-objective optimization of TSK fuzzy models
Expert Systems with Applications: An International Journal
Evolutionary algorithm based corrective process control system in glass melting process
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Application of possibilistic fuzzy regression for technology watch
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
An architecture for adaptive fuzzy control in industrial environments
Computers in Industry
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A way to automatically generate fuzzy controllers (FCs) that are optimized according to a merit figure is presented in this article. To achieve this task, a procedure based on hierarchical genetic algorithms (HGA) was developed. This procedure and the manner in which fuzzy controllers are codified into chromosomes is described. Resorting to this tool, several fuzzy controllers were constructed. The best three solutions obtained during simulation were selected for testing using an experimental prototype, which consists of an induction motor of variable load. These preliminary results are also included in the report. Based on these results, it is concluded that hierarchical genetic algorithms, though not the only, is a suitable artificial intelligence technique to face the problem of setting a fuzzy controller in a control loop without previous experience in controlling the plant. This is of help in many situations at industrial environments.