DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup

• URL: http://arxiv.org/abs/2306.03897v2
• Date: Mon, 1 Apr 2024 14:40:30 GMT
• Title: DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup
• Authors: Anubhab Ghosh, Antoine Honoré, Saikat Chatterjee,
• Abstract summary: We address the tasks of Bayesian state estimation and forecasting for a model-free process in an unsupervised learning setup. A data-driven recurrent neural network (RNN) is used in DANSE to provide the parameters of a prior of the state. We show that the proposed DANSE, without knowledge of the unscented process model and without supervised learning, provides a competitive performance against model-driven methods.
• Score: 8.167158666601553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the tasks of Bayesian state estimation and forecasting for a model-free process in an unsupervised learning setup. For a model-free process, we do not have any a-priori knowledge of the process dynamics. In the article, we propose DANSE -- a Data-driven Nonlinear State Estimation method. DANSE provides a closed-form posterior of the state of the model-free process, given linear measurements of the state. In addition, it provides a closed-form posterior for forecasting. A data-driven recurrent neural network (RNN) is used in DANSE to provide the parameters of a prior of the state. The prior depends on the past measurements as input, and then we find the closed-form posterior of the state using the current measurement as input. The data-driven RNN captures the underlying non-linear dynamics of the model-free process. The training of DANSE, mainly learning the parameters of the RNN, is executed using an unsupervised learning approach. In unsupervised learning, we have access to a training dataset comprising only a set of measurement data trajectories, but we do not have any access to the state trajectories. Therefore, DANSE does not have access to state information in the training data and can not use supervised learning. Using simulated linear and non-linear process models (Lorenz attractor and Chen attractor), we evaluate the unsupervised learning-based DANSE. We show that the proposed DANSE, without knowledge of the process model and without supervised learning, provides a competitive performance against model-driven methods, such as the Kalman filter (KF), extended KF (EKF), unscented KF (UKF), a data-driven deep Markov model (DMM) and a recently proposed hybrid method called KalmanNet. In addition, we show that DANSE works for high-dimensional state estimation.

Related papers

• Data-driven Bayesian State Estimation with Compressed Measurement of Model-free Process using Semi-supervised Learning [57.04370580292727]
  The research topic is: data-driven Bayesian state estimation with compressed measurement (BSCM) of model-free process. The dimension of the temporal measurement vector is lower than the dimension of the temporal state vector to be estimated. Two existing unsupervised learning-based data-driven methods fail to address the BSCM problem for model-free process. We develop a semi-supervised learning-based DANSE method, referred to as SemiDANSE.
  arXiv  Detail & Related papers  (2024-07-10T05:03:48Z)
• Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study [56.283944756315066]
  We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.
  arXiv  Detail & Related papers  (2024-01-09T11:54:54Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation [10.793646707711442]
  We present a framework for training generative models for density estimation. We use the score-based diffusion model to generate labeled data. Once the labeled data are generated, we can train a simple fully connected neural network to learn the generative model in the supervised manner.
  arXiv  Detail & Related papers  (2023-10-22T23:56:19Z)
• Kalman Filter for Online Classification of Non-Stationary Data [101.26838049872651]
  In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps. We introduce a probabilistic Bayesian online learning model by using a neural representation and a state space model over the linear predictor weights. In experiments in multi-class classification we demonstrate the predictive ability of the model and its flexibility to capture non-stationarity.
  arXiv  Detail & Related papers  (2023-06-14T11:41:42Z)
• Adversarial Learning Networks: Source-free Unsupervised Domain Incremental Learning [0.0]
  In a non-stationary environment, updating a DNN model requires parameter re-training or model fine-tuning. We propose an unsupervised source-free method to update DNN classification models. Unlike existing methods, our approach can update a DNN model incrementally for non-stationary source and target tasks without storing past training data.
  arXiv  Detail & Related papers  (2023-01-28T02:16:13Z)
• Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST) [1.8047694351309207]
  We develop a statistical framework for model predictions based on transfer learning, called RECaST. We mathematically and empirically demonstrate the validity of our RECaST approach for transfer learning between linear models. We examine our method's performance in a simulation study and in an application to real hospital data.
  arXiv  Detail & Related papers  (2022-11-29T19:39:47Z)
• Statistical process monitoring of artificial neural networks [1.3213490507208525]
  In machine learning, the learned relationship between the input and the output must remain valid during the model's deployment. We propose considering the latent feature representation of the data (called "embedding") generated by the ANN to determine the time when the data stream starts being nonstationary.
  arXiv  Detail & Related papers  (2022-09-15T16:33:36Z)
• Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation [51.66271681532262]
  Online Self-Acquired Knowledge Distillation (OSAKD) is proposed, aiming to improve the performance of any deep neural model in an online manner. We utilize k-nn non-parametric density estimation technique for estimating the unknown probability distributions of the data samples in the output feature space.
  arXiv  Detail & Related papers  (2021-08-26T14:01:04Z)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.