论文标题
使用矩和累积物进行统计信号表征的扩展方法：应用于使用人工神经网络的脉冲样波形识别模式改变
Extended method for Statistical Signal Characterization using moments and cumulants: Application to recognition of pattern alterations in pulse-like waveforms employing Artificial Neural Networks
论文作者
论文摘要
我们提出了一个统计程序，以表征和提取从波形中的特征，该特征可以用作人工神经网络的模式识别任务中的预处理信号阶段。这样的过程基于测量来自波形，其衍生物及其积分的30个参数的矩和累积物。该技术作为文献中存在的统计信号表征方法的扩展。 作为一种测试方法，我们使用该过程将脉冲样信号与自身的不同版本区分开，并通过频谱改变或变形。识别任务是通过对Case Sinc-，Gaussian和Chirp-Pulse波形训练的单一馈送后反向通用网络执行的。由于这些示例中获得的成功，我们可以得出结论，所提出的扩展统计信号特征方法是用于模式识别应用的有效工具。特别是，我们可以将其用作具有有限内存或计算能力的嵌入式系统中的快速预处理阶段。
We propose a statistical procedure to characterize and extract features from a waveform that can be applied as a pre-processing signal stage in a pattern recognition task using Artificial Neural Networks. Such a procedure is based on measuring a 30-parameters set of moments and cumulants from the waveform, its derivative, and its integral. The technique is presented as an extension of the Statistical Signal Characterization method existing in the literature. As a testing methodology, we used the procedure to distinguish a pulse-like signal from different versions of itself with frequency spectrum alterations or deformations. The recognition task was performed by single feed-forward back-propagation networks trained for the case Sinc-, Gaussian-, and Chirp-pulse waveform. Because of the success obtained in these examples, we can conclude that the proposed extended statistical signal characterization method is an effective tool for pattern-recognition applications. In particular, we can use it as a fast pre-processing stage in embedded systems with limited memory or computational capability.
