Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks

• URL: http://arxiv.org/abs/2109.08078v1
• Date: Thu, 16 Sep 2021 16:06:54 GMT
• Title: Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks
• Authors: Nasim Baharisangari, Kazuma Hirota, Ruixuan Yan, Agung Julius, Zhe Xu
• Abstract summary: We train neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (w GSTL) formulas. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods.
• Score: 3.2773502246783237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (wGSTL) formulas. For learning wGSTL formulas, we introduce a flexible wGSTL formula structure in which the user's preference can be applied in the inferred wGSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible wGSTL formula structure. We initially train a neural network to learn the wGSTL operators and then train a second neural network to learn the parameters in a flexible wGSTL 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, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods.

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