Learning to imitate stochastic time series in a compositional way by chaos

DOI10.1016/J.NEUNET.2009.12.006zbMath1396.68099arXiv0805.1795OpenAlexW2024967153WikidataQ45774303 ScholiaQ45774303MaRDI QIDQ1784572

Jun Tani, Jun Namikawa

Publication date: 27 September 2018

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0805.1795

zbMATH Keywords

chaosmaximum likelihood estimationrecurrent neural networkmixture of experts

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10)

Related Items (2)

A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance ⋮ Action understanding and active inference

Uses Software

• LSTM

Cites Work

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• Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems
• A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance
• Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM
• An Introduction to Symbolic Dynamics and Coding
• Chaotic itinerancy

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