论文标题
噪声信号作为自组织神经网络中的输入数据
Noise signal as input data in self-organized neural networks
论文作者
论文摘要
自组织神经网络用于分析不同分布类型（正常，三角形和均匀）的不相关的白色噪声。人为产生的噪声是通过在没有预处理的情况下聚类测得的时间信号序列样本来分析的。使用这种方法，我们首次分析了在硅2D电子系统的绝缘阶段滑动的“ Wigner-Crystal”形结构产生的当前噪声。讨论了使用该方法分析和比较通过在固态物理学和使用理论模型模拟数值数据中观察各种效果来分析和比较实验数据的可能性。
Self-organizing neural networks are used to analyze uncorrelated white noises of different distribution types (normal, triangular, and uniform). The artificially generated noises are analyzed by clustering the measured time signal sequence samples without its preprocessing. Using this approach, we analyze, for the first time, the current noise produced by a sliding "Wigner-crystal"-like structure in the insulating phase of a 2D electron system in silicon. The possibilities of using the method for analyzing and comparing experimental data obtained by observing various effects in solid-state physics and simulated numerical data using theoretical models are discussed.
