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Input-Output Selection for LSTM-Based Reduced-Order State Estimator Design

Author

Listed:
  • Sarupa Debnath

    (Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Soumya Ranjan Sahoo

    (Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Bernard Twum Agyeman

    (Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Jinfeng Liu

    (Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

Abstract

In this work, we propose a sensitivity-based approach to construct reduced-order state estimators based on recurrent neural networks (RNN). It is assumed that a mechanistic model is available but is too computationally complex for estimator design and that only some target outputs are of interest and should be estimated. A reduced-order estimator that can estimate the target outputs is sufficient to address such a problem. We introduce an approach based on sensitivity analysis to determine how to select the appropriate inputs and outputs for data collection and data-driven model development to estimate the desired outputs accurately. Specifically, we consider the long short-term memory (LSTM) neural network, a type of RNN, as the tool to train the data-driven model. Based on it, an extended Kalman filter, a state estimator, is designed to estimate the target outputs. Simulations are carried out to illustrate the effectiveness and applicability of the proposed approach.

Suggested Citation

  • Sarupa Debnath & Soumya Ranjan Sahoo & Bernard Twum Agyeman & Jinfeng Liu, 2023. "Input-Output Selection for LSTM-Based Reduced-Order State Estimator Design," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:400-:d:1033552
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    References listed on IDEAS

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    1. Andreas Rauh & Stefan Wirtensohn & Patrick Hoher & Johannes Reuter & Luc Jaulin, 2022. "Reliability Assessment of an Unscented Kalman Filter by Using Ellipsoidal Enclosure Techniques," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
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