Exploring the nonlinear dynamic behavior of artificial neural networks
Fernando José Von Zuben, M.L. de Andrade Netto · 2002
This paper explores the universal approximation capability exhibited by neural networks in the development of suitable architectures and associated training processes for nonlinear discrete-time dynamic system representation. The resulting architectures include recurrent and non recurrent multilayer neural networks and the derived training processes can be seen as optimization problems. Particular attention is given to the investigation of the dynamic behavior of a recurrent processing unit.>