DTW-NN: A novel neural network for time series recognition using dynamic alignment between inputs and weights

研究成果: Contribution to journalArticle査読

11 被引用数 (Scopus)

抄録

This paper describes a novel model for time series recognition called a Dynamic Time Warping Neural Network (DTW-NN). DTW-NN is a feedforward neural network that exploits the elastic matching ability of DTW to dynamically align the inputs of a layer to the weights. This weight alignment replaces the standard dot product within a neuron with DTW. In this way, the DTW-NN is able to tackle difficulties with time series recognition such as temporal distortions and variable pattern length within a feedforward architecture. We demonstrate the effectiveness of DTW-NNs on four distinct datasets: online handwritten characters, accelerometer-based active daily life activities, spoken Arabic numeral Mel-Frequency Cepstrum Coefficients (MFCC), and one-dimensional centroid-radii sequences from leaf shapes. We show that the proposed method is an effective general approach to temporal pattern learning by achieving state-of-the-art results on these datasets.

本文言語英語
論文番号104971
ジャーナルKnowledge-Based Systems
188
DOI
出版ステータス出版済み - 1 5 2020

All Science Journal Classification (ASJC) codes

  • 管理情報システム
  • ソフトウェア
  • 情報システムおよび情報管理
  • 人工知能

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