We introduce a hybrid neural-genetic multimodel parameter estimation algorithm. The algorithm is applied to structured system identification of nonlinear dynamical systems. The main components of the algorithm are: 1) a recurrent incremental credit assignment neural network which computes a credit function for each member of a generation of models; and 2) a genetic algorithm which uses the credit functions as selection probabilities for producing new generations of models. The neural network and genetic algorithm combination is applied to the task of finding the parameter values which minimize the total square output error: the credit function reflects the closeness of each model's output to the true system output and the genetic algorithm searches the parameter space by a divide-and-conquer technique. The algorithm is evaluated by numerical simulations of parameter estimation for a planar robotic manipulator and a waste water treatment plant.
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