Normalized RBF neural network for real-time detection of signal in the noise

Authors:
Minfen Shen;Yuzheng Zhang;Zhancheng Li;Jinyao Yang;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;Ultrasonic Institute of Shantou, Shantou, Guangdong, China;School of System Engineering, Portsmouth University, Portsmouth, U.K.
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

A new solution to real time signal detection in the noise is presented in this paper The proposed approach uses the modified RBF neural network (RBFNN) for the purposes of enhancing the ability of signal detection with low signal-to-noise radio (SNR) The characteristics and the advantages of the normalized RBFNN are discussed As an application, the extraction of singletrial evoked potentials (EP) is investigated The performance of the presented method is also addressed and compared with adaptive and common RBFNN methods Several results are included to show the applicability and the effectiveness of the new model.