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. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Lim, J. S. (2007). A neural network solution on data least square algorithm and its application for channel equalization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4493 LNCS, pp. 678–685). Springer Verlag. https://doi.org/10.1007/978-3-540-72395-0_84
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