Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters
Authors:
- Hector M. Romero UgaldeHUHector M. Romero Ugalde
- Jean-Claude CarmonaJCJean-Claude Carmona
- Victor M. AlvaradoVictor M. Alvarado
- Juan Reyes-ReyesJRJuan Reyes-Reyes
- 1 February 2013
- journal article
- research article
- Published by Elsevier in Neurocomputing
- Vol. 101, 170-180
Abstract
No abstract availableKeywords
This publication has 27 references indexed in Scilit:
- Nonlinear systems identification using dynamic multi-time scale neural networksNeurocomputing, 2011
- Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithmNeural Computing and Applications, 2011
- A differential evolution based neural network approach to nonlinear system identificationApplied Soft Computing, 2011
- Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization techniqueExpert Systems with Applications, 2010
- Black-box model identification for a continuously variable, electro-hydraulic semi-active damperMechanical Systems and Signal Processing, 2010
- Nonlinear system identification using optimized dynamic neural networkNeurocomputing, 2009
- Identification and control of nonlinear systems by a dissimilation particle swarm optimization-based Elman neural networkNonlinear Analysis: Real World Applications, 2008
- Neural networks in computational science and engineeringIEEE Computational Science and Engineering, 1996
- Pruning algorithms-a surveyIEEE Transactions on Neural Networks, 1993
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990