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Sampled-Data State Estimation for LSTM
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DOI：10.1109/TNNLS.2024.3359211.png)
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Abstract
En 中文
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.
Keywords:
Biological neural networks
Neural networks
State estimation
Recurrent neural networks
Logic gates
Design methodology
Mathematical models
Linear matrix inequalities (LMIs)
long short-term memory (LSTM)
neural networks
sampled-data system
state estimation
Journal
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8.9
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