Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
IEEE Transactions on Signal Processing
Gradient radial basis function networks for nonlinear and nonstationary time series prediction
IEEE Transactions on Neural Networks
Improved training of neural networks for the nonlinear active control of sound and vibration
IEEE Transactions on Neural Networks
Active control of vibration using a neural network
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
We have shown that duct modeling using the generalized RBF neural network (DM-RBF), which has the capability of modeling the nonlinear behavior, can suppress a variable-frequency narrow band noise of a duct more efficiently than an FX-LMS algorithm. In our method (DM-RBF), at first the duct is identified using a generalized RBF network, after that N stage of time delay of the input signal to the N generalized RBF network is applied, then a linear combiner at their outputs makes an online identification of the nonlinear system. The weights of linear combiner are updated by the normalized LMS algorithm. We have showed that the proposed method is more than three times faster in comparison with the FX-LMS algorithm with 30% lower error. Also the DM_RBF method will converge in changing the input frequency, while it makes the FX-LMS cause divergence.