论文标题
与深度学习混乱信号分类的三合会状态空间构建
Triad State Space Construction for Chaotic Signal Classification with Deep Learning
论文作者
论文摘要
提出了受众所周知的置换熵（PE）的启发，这是一种有效的混沌时间序列编码图像编码方案，三合会状态空间构建（TSSC）。 TSSC图像可以识别高阶时间模式，并在时间序列概率之外识别新的禁止区域。卷积神经网络（Convnet）广泛用于图像分类。基于TSSC图像（TSSC-CONVNET）的Convnet分类器在混乱的信号分类中非常准确且非常健壮。
Inspired by the well-known permutation entropy (PE), an effective image encoding scheme for chaotic time series, Triad State Space Construction (TSSC), is proposed. The TSSC image can recognize higher-order temporal patterns and identify new forbidden regions in time series motifs beyond the Bandt-Pompe probabilities. The Convolutional Neural Network (ConvNet) is widely used in image classification. The ConvNet classifier based on TSSC images (TSSC-ConvNet) are highly accurate and very robust in the chaotic signal classification.
