Knowledge extraction from deep convolutional neural networks applied to cyclo-stationary time-series classification

DOI10.1016/j.ins.2020.03.039zbMath1458.68193OpenAlexW3012301150MaRDI QIDQ2663548

Mariela Cerrada, Fernando Sancho, René-Vinicio Sanchez, Diego Cabrera, Chuan Li

Publication date: 19 April 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://idus.us.es/handle//11441/107225

zbMATH Keywords

fault diagnosisknowledge extractiondeep learningconvolutional neural networkcyclo-stationary time-series analysis

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial neural networks and deep learning (68T07) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (1)

Explainable time-frequency convolutional neural network for microseismic waveform classification

Uses Software

• GitHub

Cites Work

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• Classification of time series by shapelet transformation
• Neocognition: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
• The BOSS is concerned with time series classification in the presence of noise
• Springer Handbook of Bio-/Neuroinformatics
• A generalization of the sampling theorem

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