Multilayer feedforward networks are universal approximators
Neural Networks
A function estimation approach to sequential learning with neural networks
Neural Computation
Digital video processing
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Neural networks for functional approximation and system identification
Neural Computation
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Second-order Volterra system identification
IEEE Transactions on Signal Processing
Support vector method for robust ARMA system identification
IEEE Transactions on Signal Processing
Brief Fast approximate identification of nonlinear systems
Automatica (Journal of IFAC)
Nonlinear system identification via direct weight optimization
Automatica (Journal of IFAC)
A new neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
Signal detection using the radial basis function coupled map lattice
IEEE Transactions on Neural Networks
Neural data fusion algorithms based on a linearly constrained least square method
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Nonlinear spatial-temporal prediction based on optimal fusion
IEEE Transactions on Neural Networks
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Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.