Abstract
In this paper, the application of minimal resource allocation network (MRAN) trained with Unscented Kalman Filter (UKF) to the nonlinear channel equalization problems was discussed. Using novel criterion and prune strategy, the algorithm uses online learning, and has the ability to grow and prune the hidden neurons to realize a minimal network structure. Simulation results show that the equalizer is well suited for nonlinear channel equalization problems and the proposed equalizer required short training data to attain good performance.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, Y., Wu, J., Wan, G., Wu, Y. (2006). Unscented Kalman Filter-Trained MRAN Equalizer for Nonlinear Channels. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_63
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DOI: https://doi.org/10.1007/11893257_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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