Continuous-time system identification with neural networks: Model structures and fitting criteria

• URL: http://arxiv.org/abs/2006.02915v3
• Date: Tue, 31 Aug 2021 21:06:18 GMT
• Title: Continuous-time system identification with neural networks: Model structures and fitting criteria
• Authors: Marco Forgione, Dario Piga
• Abstract summary: The proposed framework is based on a representation of the system behavior in terms of continuous-time state-space models. The effectiveness of the approach is demonstrated through three case studies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems. The proposed framework is based on a representation of the system behavior in terms of continuous-time state-space models. The sequence of hidden states is optimized along with the neural network parameters in order to minimize the difference between measured and estimated outputs, and at the same time to guarantee that the optimized state sequence is consistent with the estimated system dynamics. The effectiveness of the approach is demonstrated through three case studies, including two public system identification benchmarks based on experimental data.

Related papers

• Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems [1.7406327893433848]
  This research proposes a method that leverages the physical characteristics of second-order Hamiltonian systems to constrain the baseline model. The proposed model outperforms the baseline model in identifying a minimal set of non-redundant and interpretable state variables.
  arXiv  Detail & Related papers  (2024-08-21T15:10:50Z)
• Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems [0.0]
  A predictor for partial observations is developed using an echo-state network (ESN) and time delay embedding of the partially observed state. The proposed method is theoretically justified with Taken's embedding theorem and strong observability of a nonlinear system.
  arXiv  Detail & Related papers  (2022-11-11T04:13:55Z)
• Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations [68.62843292346813]
  We propose a structured latent ODE model that captures system input variations within its latent representation. Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
  arXiv  Detail & Related papers  (2022-02-25T20:00:56Z)
• Learning to Assimilate in Chaotic Dynamical Systems [0.0]
  We introduce amortized assimilation, a framework for learning to assimilate in dynamical systems from sequences of noisy observations. We motivate the framework by extending powerful results from self-supervised denoising to the dynamical systems setting through the use of differentiable simulation.
  arXiv  Detail & Related papers  (2021-11-01T16:07:34Z)
• Supervised DKRC with Images for Offline System Identification [77.34726150561087]
  Modern dynamical systems are becoming increasingly non-linear and complex. There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. Our approach learns these basis functions using a supervised learning approach.
  arXiv  Detail & Related papers  (2021-09-06T04:39:06Z)
• Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning [49.6928533575956]
  We use neural inference to mediate between the neural System 1 and the logical System 2. Results in robust story generation and grounded instruction-following show that this approach can increase the coherence and accuracy of neurally-based generations.
  arXiv  Detail & Related papers  (2021-07-06T17:59:49Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Constrained Block Nonlinear Neural Dynamical Models [1.3163098563588727]
  Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems.
  arXiv  Detail & Related papers  (2021-01-06T04:27:54Z)
• An Ode to an ODE [78.97367880223254]
  We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the group O(d) This nested system of two flows provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem.
  arXiv  Detail & Related papers  (2020-06-19T22:05:19Z)
• Active Learning for Nonlinear System Identification with Guarantees [102.43355665393067]
  We study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs. We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data. We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.
  arXiv  Detail & Related papers  (2020-06-18T04:54:11Z)
• Data-Driven Verification under Signal Temporal Logic Constraints [0.0]
  We consider systems under uncertainty whose dynamics are partially unknown. Our aim is to study satisfaction of temporal logic properties by trajectories of such systems. We employ Bayesian inference techniques to associate a confidence value to the satisfaction of the property.
  arXiv  Detail & Related papers  (2020-05-08T08:32:30Z)

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.