Normalized RBF neural network for tracking transient signal in the noise

Authors:
Minfen Shen;Yuzheng Zhang;Zhancheng Li;Patch Beadle
Affiliations:
Key Lab. of Guangdong, Shantou University, Guangdong, China;Key Lab. of Guangdong, Shantou University, Guangdong, China;Key Lab. of Guangdong, Shantou University, Guangdong, China;School of System Engineering, Portsmouth University, Portsmouth, U.K.
Venue:
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
Year:
2004

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Abstract

A novel approach is proposed to solve the problem of detecting the signal in the noise using a modified RBF neural network (RBFNN). The RBFNN is normalized to obtain optimal behavior of noise suppression even at low SNR. The performance of the proposed scheme is also evaluated with both MSE and the tracking ability. Several experimental results provide the convergent evidence to show that the method can significantly enhance the SNR and successfully track the variation of the signal such as evoket potential.