Learning to reproduce fluctuating behavioral sequences using a dynamic neural network model with time-varying variance estimation mechanism

Shingo Murata, Jun Namikawa, Hiroaki Arie, Jun Tani, Shigeki Sugano

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

This study shows that a novel type of recurrent neural network model can learn to reproduce fluctuating training sequences by inferring their stochastic structures. The network learns to predict not only the mean of the next input state, but also its time-varying variance. The network is trained through maximum likelihood estimation by utilizing the gradient descent method, and the likelihood function is expressed as a function of both the predicted mean and variance. In a numerical experiment, in order to evaluate the performance of the model, we first tested its ability to reproduce fluctuating training sequences generated by a known dynamical system that were perturbed by Gaussian noise with state-dependent variance. Our analysis showed that the network can reproduce the sequences by predicting the variance correctly. Furthermore, the other experiment showed that a humanoid robot equipped with the network can learn to reproduce fluctuating tutoring sequences by inferring latent stochastic structures hidden in the sequences.

本文言語English
ホスト出版物のタイトル2013 IEEE 3rd Joint International Conference on Development and Learning and Epigenetic Robotics, ICDL 2013 - Electronic Conference Proceedings
DOI
出版ステータスPublished - 2013 12 31
外部発表はい
イベント2013 IEEE 3rd Joint International Conference on Development and Learning and Epigenetic Robotics, ICDL 2013 - Osaka, Japan
継続期間: 2013 8 182013 8 22

出版物シリーズ

名前2013 IEEE 3rd Joint International Conference on Development and Learning and Epigenetic Robotics, ICDL 2013 - Electronic Conference Proceedings

Conference

Conference2013 IEEE 3rd Joint International Conference on Development and Learning and Epigenetic Robotics, ICDL 2013
国/地域Japan
CityOsaka
Period13/8/1813/8/22

ASJC Scopus subject areas

  • 人工知能
  • 人間とコンピュータの相互作用
  • ソフトウェア

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