Contact Info
Department of Applied Mathematics
University of Waterloo
Waterloo, Ontario
Canada N2L 3G1
Phone: 519-888-4567, ext. 32700
Fax: 519-746-4319
PDF files require Adobe Acrobat Reader
MS Teams (please email amgrad@uwaterloo.ca for the meeting link)
Akihiro Takigawa| Applied Mathematics, University of Waterloo
A comparison of the Kalman filter and recurrent neural networks for state estimation of dynamical systems
The study of dynamical systems is of great interest in many fields, with a wide range of applications. In some cases, these dynamical systems may be affected by noise and the availability of measurements may be limited. State estimations methods which can account for these challenges are valuable tools in analyzing these systems. While for linear systems the standard method is by using an algorithm called the Kalman filter, data-driven methods employing the versatility of artificial neural networks have also been proposed. In this thesis, we first introduce state estimation using the Kalman filter. Next, we provide an overview of a type of artificial neural network called recurrent neural networks (RNNs), which are particularly suited for tasks on time series data. We finally present the results of implementing RNN-based estimators for a number of dynamical systems with comparisons to Kalman filtering.
Contact Info
Department of Applied Mathematics
University of Waterloo
Waterloo, Ontario
Canada N2L 3G1
Phone: 519-888-4567, ext. 32700
Fax: 519-746-4319
PDF files require Adobe Acrobat Reader
The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is co-ordinated within the Office of Indigenous Relations.