# Triad State Space Construction for Chaotic Signal Classification with Deep Learning

Source: [Triad State Space Construction for Chaotic Signal Classification with Deep Learning](<https://www.sota2.com/research/paper/68fa3bbc1e209131ce8d5273>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T00:45:55.387Z

Authors: Yadong Zhang, Xin Chen

Published: 2020-03-26T14:15:41.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.11931>)

[PDF](<https://arxiv.org/pdf/2003.11931.pdf>)