Key words: Robust Identification, Non Linear Identification, Genetic Algorithms
نویسندگان
چکیده
In this article, a procedure for characterizing the feasible parameter set of nonlinear models with a membershipset uncertainty description is provided. A specific Genetic Algorithm denominated ε-GA has been developed, based on Evolutionary Algorithm for Multiobjective Optimization, to find the global minima of the multimodal functions appeared when the robust identification problem is formulated. These global minima define the contour of the feasible parameter set. The procedure let someone to obtain the feasible parameter non-convex even disjoint set. It is not necessary for the model to be differentiable respect to the unknown parameters. It is presented an example which determines the feasible parameter set of a nonlinear model of a thermal process. In this case, noise is affecting to the output process (interior temperature) and besides model errors exist.
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