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    ASIA unversity > 資訊學院 > 光電與通訊學系 > 期刊論文 >  Item 310904400/17111

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/17111

    Title: A polynomial-perceptron based decision feedback equalizer with a robust learning algorithm
    Authors: 張清濠;Chang, Ching-Haur
    Contributors: 光電與通訊學系
    Keywords: Robust learning algorithm;Polynomial-perceptron based DFE;lp-norm error criterion
    Date: 1995-11
    Issue Date: 2012-11-26 10:22:05 (UTC+8)
    Abstract: A new equalization scheme, including a decision feedback equalizer (DFE) equipped with polynomial-perceptron model of nonlinearities and a robust learning algorithm using lp-norm error criterion with p < 2, is presented in this paper. This equalizer exerts the benefit of using a DFE and achieves the required nonlinearities in a single-layer net. This makes it easier to train by a stochastic gradient algorithm in comparison with a multi-layer net. The algorithm is robust to aberrant noise for the addressed equalizer and, hence, converges much faster in comparison with the l2-norm. A detailed performance analysis considering possible numerical problem for p < 1 is given in this paper. Computer simulations show that the scheme has faster convergence rate and satisfactory bit error rate (BER) performance. It also shows that the new equalizer is capable of approaching the performance achieved by a minimum BER equalizer.
    Appears in Collections:[光電與通訊學系] 期刊論文

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