一种基于粒子群算法的自适应信道均衡器

Sandhya Yogi, K. Subhashini, J. Satapathy, Shiv Kumar
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引用次数: 3

摘要

在这里,我们提出了一种用于信道均衡的替代神经网络结构,称为功能链路神经网络(FLANN)。与前馈神经网络结构(即多层感知器(MLP))相比,FLANN基本上是一个单层结构,其中通过非线性函数展开增强输入模式来引入非线性。提出了一种利用粒子群算法训练flann的新方法。为了提高均衡器的性能,对神经元结构进行了改进。从结果可以看出,该结构提高了flann在区分接收数据方面的分类能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel PSO based adaptive channel equalizer using a modified ANN structure
Here we have presented an alternate ANN structure called functional link ANN (FLANN) for channel equalization. In contrast to a feed forward ANN structure i.e. a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which non-linearity is introduced by enhancing the input pattern with nonlinear function expansion. A novel method of training the FLANNs using PSO Algorithm is described. The neuron structure is modified to improve the performance of the equalizer. From the results it can be noted that the proposed structure improves the classification capability of the FLANNs in differentiating the received data.
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