1. Implementation of Kalman Filtering with Spiking Neural Networks.
- Author
-
Juárez-Lora, Alejandro, García-Sebastián, Luis M., Ponce-Ponce, Victor H., Rubio-Espino, Elsa, Molina-Lozano, Herón, and Sossa, Humberto
- Subjects
- *
ARTIFICIAL neural networks , *MACHINE learning , *KALMAN filtering , *COMPUTER architecture , *NEURAL circuitry , *NONLINEAR systems , *SYSTEM dynamics - Abstract
A Kalman filter can be used to fill space–state reconstruction dynamics based on knowledge of a system and partial measurements. However, its performance relies on accurate modeling of the system dynamics and a proper characterization of the uncertainties, which can be hard to obtain in real-life scenarios. In this work, we explore how the values of a Kalman gain matrix can be estimated by using spiking neural networks through a combination of biologically plausible neuron models with spike-time-dependent plasticity learning algorithms. The performance of proposed neural architecture is verified with simulations of some representative nonlinear systems, which show promising results. This approach traces a path for its implementation in neuromorphic analog hardware that can learn and reconstruct partial and changing dynamics of a system without the massive power consumption that is typically needed in a Von Neumann-based computer architecture. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF