Applying Spiking Neural Nets to Noise Shaping
Applying Spiking Neural Nets to Noise Shaping
复制标题
将尖峰神经网络应用于噪声整形
DOI：
10.1093/ietisy/e88-d.8.1885
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
R. Schüffny
中科院分区：
文献类型：
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作者：
C. Mayr;R. Schüffny
近年来，人们越来越关注脉冲神经网络中的信息传输机制。特别是这些网络的噪声整形特性及其与Delta-Sigma调制器的相似性受到了广泛的关注。然而，在这一领域所做的研究很少集中在这些网络中的权重对噪声整形特性和网络输出信号的后处理的影响上。本文关注的是网络运行的各种模式，以及系统生成网络权重可能产生的有利和不利影响。此外，还介绍了一种对尖峰输出信号进行后处理的方法，使输出信号更符合传统的Δ-Σ调制器。这项研究的相关性，工业应用的神经网络的积木过采样A/D转换器。此外，还列出了进一步的争论点，必须进行深入研究，以增加上述脉冲神经网络的适用性。
In recent years, there has been an increased focus on the mechanics of information transmission in spiking neural networks. Especially the Noise Shaping properties of these networks and their similarity to Delta-Sigma Modulators has received a lot of attention. However, very little of the research done in this area has focused on the effect the weights in these networks have on the Noise Shaping properties and on post-processing of the network output signal. This paper concerns itself with the various modes of network operation and beneficial as well as detrimental effects which the systematic generation of network weights can effect. Also, a method for post-processing of the spiking output signal is introduced, bringing the output signal more in line with conventional Delta-Sigma Modulators. Relevancy of this research to industrial application of neural nets as building blocks of oversampled A/D converters is shown. Also, further points of contention are listed, which must be thoroughly researched to add to the above mentioned applicability of spiking neural nets.