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

Sarupa Debnath, Soumya Ranjan Sahoo, Bernard Twum Agyeman and Jinfeng Liu ()
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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

Mathematics, 2023, vol. 11, issue 2, 1-18

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.

Keywords: sensitivity analysis; state estimation; reduced-order state estimation; extended Kalman filter (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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