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Normalized RBF Neural Network for Tracking Transient Signal in the Noise

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Parallel and Distributed Computing: Applications and Technologies (PDCAT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3320))

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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.

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Author information

Authors and Affiliations

  1. Key Lab. of Guangdong, Shantou University, Guangdong, 515063, China

    Minfen Shen, Yuzheng Zhang & Zhancheng Li

  2. School of System Engineering, Portsmouth University, Portsmouth, U.K.

    Patch Beadle

Authors
  1. Minfen Shen
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  2. Yuzheng Zhang
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  3. Zhancheng Li
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  4. Patch Beadle
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Editor information

Editors and Affiliations

  1. Nanyang Centre for Supercomputing and Visualisation School of Mechanical and Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639 798, Singapore

    Kim-Meow Liew

  2. School of Computer Science, The University of Adelaide,  

    Hong Shen

  3. Asia Pacific Science and Technology Center, Sun Microsystems Inc., 50 Nanyang Avenue, N3-1c-10, 639798, Singapore

    Simon See

  4. School of Computer Engineering, Nanyang Technological University, 639798, Singapore

    Wentong Cai

  5. Southwest Jiaotong University,  

    Pingzhi Fan

  6. Tohoku University ,  

    Susumu Horiguchi

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© 2004 Springer-Verlag Berlin Heidelberg

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Shen, M., Zhang, Y., Li, Z., Beadle, P. (2004). Normalized RBF Neural Network for Tracking Transient Signal in the Noise. In: Liew, KM., Shen, H., See, S., Cai, W., Fan, P., Horiguchi, S. (eds) Parallel and Distributed Computing: Applications and Technologies. PDCAT 2004. Lecture Notes in Computer Science, vol 3320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30501-9_44

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  • DOI: https://doi.org/10.1007/978-3-540-30501-9_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24013-6

  • Online ISBN: 978-3-540-30501-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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