Long-term prediction of hydraulic system dynamics via structured recurrent neural networks

This work presents a methodology for designing neural networks to predict the behavior of nonlinear dynamical systems with the guidance of a priori knowledge on the physical systems. The traditional neural network development techniques are known to have considerable disadvantages including tedious...

Full description

Saved in:
Bibliographic Details
Published in2011 IEEE International Conference on Mechatronics pp. 330 - 335
Main Authors Kilic, E., Dolen, M., Koku, A. B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2011
Subjects
Artificial neural networks
Convergence
dynamic models
hydraulic systems
Iron
long-term prediction
Recurrent neural networks
System identification
Online AccessGet full text

Cover

More Information
Summary:This work presents a methodology for designing neural networks to predict the behavior of nonlinear dynamical systems with the guidance of a priori knowledge on the physical systems. The traditional neural network development techniques are known to have considerable disadvantages including tedious design process, long training periods, and most notably convergence/stability problems for most real world applications. The presented approach, which circumvents such bottlenecks, is especially useful in developing efficient neural network models when full-scale models are not available. This study illustrates the application of the method on a highly nonlinear hydraulic servo-system so to estimate accurately the chamber pressures of its hydraulic piston in extended time periods.
ISBN:9781612849829
1612849822
DOI:10.1109/ICMECH.2011.5971305