Neural Variational Identification and Filtering for Stochastic Non-linear Dynamical Systems with Application to Non-intrusive Load Monitoring

Henning Lange; Mario Bergés; J. Zico Kolter

ICASSP 2019accepted0 citations

Neural Variational Identification and Filtering for Stochastic Non-linear Dynamical Systems with Application to Non-intrusive Load Monitoring

Henning Lange, Mario Bergés, J. Zico Kolter

Abstract

In this paper, an algorithm for performing System Identification and inference of the filtering recursion for stochastic non-linear dynamical systems is introduced. Additionally, the algorithm allows for enforcing domain-constraints of the state variable. The algorithm makes use of an approximate inference technique called Variational Inference in conjunction with Deep Neural Networks as the optimization engine. Although general in its nature, the algorithm is evaluated in the context of Non-Intrusive Load Monitoring, the problem of inferring the operational state of individual electrical appliances given aggregate measurements of electrical power collected in a home.

BibTeX

@inproceedings{icassp2019_neuralvariationa,
  title = {Neural Variational Identification and Filtering for Stochastic Non-linear Dynamical Systems with Application to Non-intrusive Load Monitoring},
  author = {Henning Lange and Mario Bergés and J. Zico Kolter},
  booktitle = {ICASSP 2019},
  year = {2019}
}