Title :
Exploring the nonlinear dynamic behavior of artificial neural networks
Author :
Von Zuben, Fernando J. ; De Andrade Netto, Márcio L.
Author_Institution :
Sch. of Electr. Eng., State Univ. of Campinas, Brazil
fDate :
27 Jun-2 Jul 1994
Abstract :
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
Keywords :
learning (artificial intelligence); optimisation; recurrent neural nets; artificial neural networks; multilayer neural networks; nonlinear discrete-time dynamic system representation; nonlinear dynamic behavior; optimization problems; training processes; universal approximation capability; Artificial neural networks; Computer architecture; Delay effects; Ear; Multi-layer neural network; Multilayer perceptrons; Neural networks; Nonlinear dynamical systems; Recurrent neural networks; Signal processing;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374319
