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A Neural Network Solution on Data Least Square Algorithm and Its Application for Channel Equalization

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Advances in Neural Networks – ISNN 2007 (ISNN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4493))

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Abstract

Using the neural network model for oriented principal component analysis (OPCA), we propose a solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. In this paper, We applied this neural network model to channel equalization. Simulations show that DLS outperforms ordinary least square in channel equalization problems.

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References

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Authors and Affiliations

  1. Dept. of Electronics Eng., Sejong University, Kunja, Kwangjin, 98,143-747, Seoul, Korea

    Jun-Seok Lim

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  1. Jun-Seok Lim
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Editor information

Derong Liu Shumin Fei Zengguang Hou Huaguang Zhang Changyin Sun

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© 2007 Springer Berlin Heidelberg

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Lim, JS. (2007). A Neural Network Solution on Data Least Square Algorithm and Its Application for Channel Equalization. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_84

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  • DOI: https://doi.org/10.1007/978-3-540-72395-0_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72394-3

  • Online ISBN: 978-3-540-72395-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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