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DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models
KTH Royal Institute of Technology.ORCID iD: 0000-0001-6612-6923
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. (Systems and Control)ORCID iD: 0000-0001-5474-7060
KTH Royal Institute of Technology.ORCID iD: 0000-0003-2638-6047
KTH Royal Institute of Technology.ORCID iD: 0000-0002-9368-3079
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks in learning an estimator. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks -- long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator. 

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Signal Processing Control Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
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URN: urn:nbn:se:uu:diva-481031OAI: oai:DiVA.org:uu-481031DiVA, id: diva2:1685026
Available from: 2022-07-31 Created: 2022-07-31 Last updated: 2022-11-02Bibliographically approved

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Abdalmoaty, Mohamed

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Anubhab, GhoshAbdalmoaty, MohamedChatterjee, SaikatHjalmarsson, Håkan
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