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Neural Network Modeling and Identification of Dynamical Systems
presents a new approach on how to obtain the adaptive neural
network models for complex systems that are typically found in
real-world applications. The book introduces the theoretical
knowledge available for the modeled system into the purely
empirical black box model, thereby converting the model to the gray
box category. This approach significantly reduces the dimension of
the resulting model and the required size of the training set. This
book offers solutions for identifying controlled dynamical systems,
as well as identifying characteristics of such systems, in
particular, the aerodynamic characteristics of aircraft.
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