Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks

Nasim Baharisangari, Kazuma Hirota, Ruixuan Yan, Agung Julius, Zhe Xu

Research output: Contribution to journalArticlepeer-review

Abstract

Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this letter, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (w-GSTL) formulas. For learning w-GSTL formulas, we introduce a flexible w-GSTL formula structure in which the user's preference can be applied in the inferred w-GSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible w-GSTL formula structure. We initially train a neural network to learn the w-GSTL operators, and then train a second neural network to learn the parameters in a flexible w-GSTL formula structure. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms. We compare the performance of the proposed framework with three baseline classification methods including K-nearest neighbors, decision trees, support vector machine, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods.

Original languageEnglish (US)
Pages (from-to)2096-2101
Number of pages6
JournalIEEE Control Systems Letters
Volume6
DOIs
StatePublished - 2022
Externally publishedYes

Keywords

  • Neural networks
  • weighted graph-based signal temporal logic

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization

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