Training multilayer perceptrons with the extended Kalman algorithm
Advances in neural information processing systems 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Robust minimum variance beamforming
IEEE Transactions on Signal Processing
Multiweight optimization in optimal bounding ellipsoid algorithms
IEEE Transactions on Signal Processing
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ellipsoidal calculus based on propagation and fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An algorithmic approach to adaptive state filtering using recurrent neural networks
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
WSEAS Transactions on Computers
Identifying dynamic systems with polynomial nonlinearities in the errors-in-variables context
WSEAS TRANSACTIONS on SYSTEMS
Algorithms for estimation in distributed parameter systems based on sensor networks and ANFIS
WSEAS TRANSACTIONS on SYSTEMS
ACMOS'09 Proceedings of the 11th WSEAS international conference on Automatic control, modelling and simulation
Hi-index | 0.00 |
Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as the Kalman filter algorithm. Optimal bounded ellipsoid algorithm has some better properties than the Kalman filter training, one is that the noise is not required to be Guassian. In this paper optimal bounded ellipsoid algorithm is applied train the weights of a feedforward neural network for nonlinear system identification. Both hidden layers and output layers can be updated. In order to improve robustness of the optimal of the optimal bounded ellipsoid algorithm, dead-zone is applied to this algorithm. From a dynamic systems point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. Two examples where provided which illustrate the effectiveness of the suggested algorithm based on simulations.