Cargando…

Implementation of Kalman Filtering with Spiking Neural Networks

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 sc...

Descripción completa

Detalles Bibliográficos
Autores principales: Juárez-Lora, Alejandro, García-Sebastián, Luis M., Ponce-Ponce, Victor H., Rubio-Espino, Elsa, Molina-Lozano, Herón, Sossa, Humberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695172/
https://www.ncbi.nlm.nih.gov/pubmed/36433442
http://dx.doi.org/10.3390/s22228845
Descripción
Sumario: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.