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首页> 外文期刊>International journal of mobile computing and multimedia communications >Multilayer Perceptron Based Equalizer with an Improved Back Propagation Algorithm for Nonlinear Channels
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Multilayer Perceptron Based Equalizer with an Improved Back Propagation Algorithm for Nonlinear Channels

机译：基于多层感知器的均衡器，带有改进的反向传播算法，用于非线性信道

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Neural network based equalizers can easily compensate channel impairments; such additive noise and inter symbol interference (ISI). The authors present a new approach to improve the training efficiency of the multilayer perceptron (MLP) based equalizer. Their improvement consists on modifying the back propagation (BP) algorithm, by adapting the activation function in addition to the other parameters of the MLP structure. The authors report on experiment results evaluating the performance of the proposed approach namely the back propagation with adaptive activation function (BPAAF) next to the BP algorithm. To further prove its effectiveness, the proposed approach is also compared beside a so known nonlinear equalizer explicitly the multilayer perceptron with decision feedback equalizer MLPDFE. The authors consider various performance measures specifically: signal resorted quality, lower steady state MSE reached and minimum bit error rate (BER) achieved, where nonlinear channel equalization problems are employed.
机译：基于神经网络的均衡器可以轻松补偿信道损伤；此类附加噪声和符号间干扰（ISI）。作者提出了一种新的方法来提高基于多层感知器（MLP）的均衡器的训练效率。它们的改进包括通过修改激活功能以及MLP结构的其他参数来修改反向传播（BP）算法。作者在实验结果报告中对所提出方法的性能进行了评估，该方法即紧随BP算法的具有自适应激活函数（BPAAF）的反向传播。为了进一步证明其有效性，还将所提出的方法与已知的非线性均衡器进行了比较，明确地将多层感知器与决策反馈均衡器MLPDFE进行了比较。作者特别考虑了各种性能指标：信号重用质量，达到较低的稳态MSE并实现了最小误码率（BER），其中采用了非线性信道均衡问题。

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