Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Robust adaptive control
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Robust neural-network control of rigid-link electrically driven robots
IEEE Transactions on Neural Networks
Nonlinear adaptive trajectory tracking using dynamic neural networks
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
A novel deniable authentication protocol using generalized ElGamal signature scheme
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
A recurrent neuro-fuzzy system and its application in inferential sensing
Applied Soft Computing
Local model network identification for online engine modelling
Information Sciences: an International Journal
| Hi-index | 0.00 |
In this paper continuous-time recurrent multilayer perceptrons (RMLP) are proposed to identify nonlinear systems. Using the function approximation theorem for multilayer perceptrons (MLP), we conclude that RMLP can approximate any dynamic system in any degree of accuracy. By means of a Lyapunov-like analysis, a stable learning algorithm for RMLP is determined. The suggested learning algorithm is similar to the well-known backpropagation nile of the MLP but with an additional term which assure the stability of identification error.