TY - JOUR
T1 - Neural network training with optimal bounded ellipsoid algorithm
AU - de Jesús Rubio, José
AU - Yu, Wen
AU - Ferreyra, Andrés
PY - 2009/9
Y1 - 2009/9
N2 - 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 Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied in training the weights of the feedforward neural network for nonlinear system identification. Both hidden layers and output layers can be updated. From a dynamic system point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. Two simulations give the effectiveness of the suggested algorithm.
AB - 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 Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied in training the weights of the feedforward neural network for nonlinear system identification. Both hidden layers and output layers can be updated. From a dynamic system point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. Two simulations give the effectiveness of the suggested algorithm.
KW - Ellipsoid algorithm
KW - Identification
KW - Neural networks
KW - Stability
UR - http://www.scopus.com/inward/record.url?scp=70349593217&partnerID=8YFLogxK
U2 - 10.1007/s00521-008-0203-5
DO - 10.1007/s00521-008-0203-5
M3 - Artículo
SN - 0941-0643
VL - 18
SP - 623
EP - 631
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 6
ER -