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Journal Article
Sampled-Data State Estimation for LSTM
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- Authors
- Yongsik Jin, S. M. Lee
- Issue Date
- 2025-02
- Citation
- IEEE Transactions on Neural Networks and Learning Systems, v.36, no.2, pp.2300-2313
- ISSN
- 2162-237X
- Publisher
- IEEE Computational Intelligence Society
- Language
- English
- Type
- Journal Article
- Abstract
- This article first introduces a sampled-data state estimator design method for continuous-time long short-term memory (LSTM) neural networks with irregularly sampled output. To this end, the structure of the LSTM is addressed to obtain its dynamic equation. As a result, the LSTM neural network is modeled as a continuous-time linear parameter-varying system that is dependent on the gate units. For this system, the sampled-data Luenberger-and Arcak-type state estimator design methods are presented in terms of linear matrix inequalities (LMIs) by using the properties of the gate units. Lastly, the proposed method not only provides a numerical example for analyzing absolute stability but also demonstrates it in practice by applying a pre-trained behavior generation model of a robot manipulator.
- KSP Keywords
- Absolute stability, Behavior Generation, Continuous-Time, Design method, Dynamic equation, Estimator design, Generation model, Linear matrix inequalities(LMI), Matrix inequality, Robot manipulator, State estimator