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A theoretical study of linear and nonlinear equalization in nonlinear magnetic storage channels

机译:非线性磁存储通道中线性和非线性均衡的理论研究

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We present methods to systematically design a feedforward neural-network detector from the knowledge of the channel characteristics. Its performance is compared with the conventional linear equalizer in a magnetic recording channel suffering from signal-dependent noise and nonlinear intersymbol interference. The superiority of the nonlinear schemes are clearly observed in all cases studied, especially in the presence of severe nonlinearity and noise. We also show that the decision boundaries formed by a theoretically derived neural-network classifier are geometrically close to those of a neural network trained by the backpropagation algorithm. The approach in this work is suitable for quantifying the gain in using a neural-network method as opposed to linear methods in the classification of noisy patterns.
机译:我们提出了根据通道特性的知识系统设计前馈神经网络检测器的方法。在遭受信号相关噪声和非线性符号间干扰的磁记录通道中,将其性能与常规线性均衡器进行了比较。在所有研究的案例中,尤其是在存在严重的非线性和噪声的情况下,都清楚地观察到了非线性方案的优越性。我们还表明,从理论上推导的神经网络分类器形成的决策边界在几何上接近于由反向传播算法训练的神经网络的决策边界。这项工作中的方法适合于使用神经网络方法量化增益,这与对噪声模式进行分类的线性方法相对。

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