Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Genetic algorithms for learning the rule base of fuzzy logic controller
Fuzzy Sets and Systems
Universal approximation by hierarchical fuzzy systems
Fuzzy Sets and Systems
A New Type of Fuzzy Logic System for Adaptive Modeling and Control
Proceedings of the International Conference on Computational Intelligence, Theory and Applications
Fuzzy multi-layer perceptron, inferencing and rule generation
IEEE Transactions on Neural Networks
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Fuzzy controller generating procedures when using crisp input-output data produce the necessary system in two steps: first they produce a starting rule set and then they tune the parameters that influence the approximation with a learning algorithm. Other solutions work under special conditions as hybrid neuro-fuzzy systems improving the approximation with a gradient based learning algorithm (e.g. in the case of monotonous membership functions), or use the methods of the genetic algorithms to generate the fuzzy controller. This article demonstrates a new method which reduces the problem to a classification task and carries out the generation of the rules and the tuning of the system in a single step.