Radial basis function network applied to the linearization of a voltage controlled oscillator
Proceedings of the 20th annual conference on Integrated circuits and systems design
RBF circuits based on folded cascode differential pairs
Proceedings of the 21st annual symposium on Integrated circuits and system design
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A very fast neural learning for classification using only new incoming datum
IEEE Transactions on Neural Networks
Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation
Computers in Biology and Medicine
Neural Processing Letters
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In this paper, a higher-order-statistics (HOS)-based radial basis function (RBF) network for signal enhancement is introduced. In the proposed scheme, higher order cumulants of the reference signal were used as the input of HOS-based RBF. An HOS-based supervised learning algorithm, with mean square error obtained from higher order cumulants of the desired input and the system output as the learning criterion, was used to adapt weights. The motivation is that the HOS can effectively suppress Gaussian and symmetrically distributed non-Gaussian noise. The influence of a Gaussian noise on the input of HOS-based RBF and the HOS-based learning algorithm can be mitigated. Simulated results indicate that HOS-based RBF can provide better performance for signal enhancement under different noise levels, and its performance is insensitive to the selection of learning rates. Moreover, the efficiency of HOS-based RBF under the nonstationary Gaussian noise is stable