Higher-Order-Statistics-Based Radial Basis Function Networks for Signal Enhancement
Resource
IEEE Neural Network 18 (3): 823-832
Journal
IEEE Neural Network
Journal Volume
18
Journal Issue
3
Pages
823-832
Date Issued
2007
Date
2007
Author(s)
Abstract
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. © 2007 IEEE.
Subjects
Gaussian noise; Higher order statistics (HOS); Radial basis function (RBF) networks; Signal enhancement
Other Subjects
Computer simulation; Gaussian noise (electronic); Learning algorithms; Mean square error; Signal processing; Statistics; Higher-order-statistics (HOS); Signal enhancement; Radial basis function networks; algorithm; article; artificial intelligence; artificial neural network; automated pattern recognition; computer simulation; decision support system; evaluation; information retrieval; methodology; normal distribution; signal processing; statistical model; Algorithms; Artificial Intelligence; Computer Simulation; Decision Support Techniques; Information Storage and Retrieval; Models, Statistical; Neural Networks (Computer); Normal Distribution; Pattern Recognition, Automated; Signal Processing, Computer-Assisted
Type
journal article
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