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.
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Shen, M., Zhang, Y., Li, Z., Yang, J., Beadle, P. (2004). Normalized RBF Neural Network for Real-Time Detection of Signal in the Noise. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_129
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DOI: https://doi.org/10.1007/978-3-540-30549-1_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
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