Neural Network Modeling and Identification of Dynamical Systems
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.
One of the key elements of the development process for new engineering systems is the
formation of mathematical and computer models that provide solutions to the problems
of creating and using appropriate technical systems. As the complexity of the created
systems grows, so do the requirements for their models, as well as for the funds raised
for the development of these models.
At present, the possibilities of mathematical and computer modeling are lagging behind the
needs of a number of engineering fields, such as aerospace technology, robotics, and control of
complex production processes.