Abstract
The use of sensitivity information from a neuro-fuzzy model for the purpose of optimization is investigated in this paper. This approach permits the application of classic deterministic or hybrid optimization methods in establishing the global minimum of any approximated objective function using neuro-fuzzy modeling. For nondifferentiable functions this approach is of great benefit. An analytical problem and the TEAM 22 benchmark problem are investigated. Results using the genetic algorithm method and the sequential quadratic programming method in sequence show the usefulness of the formulation.
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