HYBRID NEURAL-GENETIC MULTIMODAL PARAMETER-ESTIMATION ALGORITHM

We introduce a hybrid neural-genetic multimodel parameter estimation a lgorithm. 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 (ICRA) 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 s election probabilities for producing new generations of models. The ne ural 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 out put to the true system output and the genetic algorithm searches the p arameter space by a divide-and-conquer technique. The algorithm is eva luated by numerical simulations of parameter estimation for a planar r obotic manipulator and a waste water treatment plant.