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Neural Networks Modelling and Control: Applications for Unknown
Nonlinear Delayed Systems in Discrete Time focuses on modeling and
control of discrete-time unknown nonlinear delayed systems under
uncertainties based on Artificial Neural Networks. First, a
Recurrent High Order Neural Network (RHONN) is used to identify
discrete-time unknown nonlinear delayed systems under
uncertainties, then a RHONN is used to design neural observers for
the same class of systems. Therefore, both neural models are used
to synthesize controllers for trajectory tracking based on two
methodologies: sliding mode control and Inverse Optimal Neural
Control. As well as considering the different neural control models
and complications that are associated with them, this book also
analyzes potential applications, prototypes and future trends.
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