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GA-P BASED MODELING OF NONLINEAR DYNAMIC SYSTEMS
Lopez, A.M.* Lopez, H.* Sanchez, L.**
* University of Oviedo. Electrical Engineering Department.
C. Universitario de Viesques, Ed. Departamental N° 2, 33203, Gijon,
Asturias, Spain.
e-mail: [antonio,hilario]@isa.uniovi.es
** University of Oviedo, Computer Science Department.
C. Universitario de Viesques, Ed. Departamental N° 1, 33202, Gijon,
Asturias, Spain.
e-mail: luciano@lsi.uniovi.es
Model construction is usually guided by a trial-error process, where each iteration is divided into two steps: (i) physical modeling and (ii) identification.
Genetic programming has been applied to automate this process in different ways. One of the most complete approaches is the described in project SMOG, where a set of model structures is evolved, being the set of parameters of each model optimized by means of classical methods. In this paper, a GA-P algorithm (a hybrid between genetic algorithms and genetic programming) is applied to the task permitting the evolution in parallel of model structures and parameters.
Keywords: Dynamic systems, nonlinear systems, identification algorithms, stochastic modelling, block diagrams, directed graphs, artificial intelligence, genetic algorithms
Session slot T-We-M21: Posters of Modelling, Identification and Discrete Systems/Area code 3a : Modelling, Identification and Signal Processing
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