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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...

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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
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author Juárez-Lora, Alejandro
García-Sebastián, Luis M.
Ponce-Ponce, Victor H.
Rubio-Espino, Elsa
Molina-Lozano, Herón
Sossa, Humberto
author_facet Juárez-Lora, Alejandro
García-Sebastián, Luis M.
Ponce-Ponce, Victor H.
Rubio-Espino, Elsa
Molina-Lozano, Herón
Sossa, Humberto
author_sort Juárez-Lora, Alejandro
collection PubMed
description 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.
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spelling pubmed-96951722022-11-26 Implementation of Kalman Filtering with Spiking Neural Networks Juárez-Lora, Alejandro García-Sebastián, Luis M. Ponce-Ponce, Victor H. Rubio-Espino, Elsa Molina-Lozano, Herón Sossa, Humberto Sensors (Basel) Article 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. MDPI 2022-11-16 /pmc/articles/PMC9695172/ /pubmed/36433442 http://dx.doi.org/10.3390/s22228845 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Juárez-Lora, Alejandro
García-Sebastián, Luis M.
Ponce-Ponce, Victor H.
Rubio-Espino, Elsa
Molina-Lozano, Herón
Sossa, Humberto
Implementation of Kalman Filtering with Spiking Neural Networks
title Implementation of Kalman Filtering with Spiking Neural Networks
title_full Implementation of Kalman Filtering with Spiking Neural Networks
title_fullStr Implementation of Kalman Filtering with Spiking Neural Networks
title_full_unstemmed Implementation of Kalman Filtering with Spiking Neural Networks
title_short Implementation of Kalman Filtering with Spiking Neural Networks
title_sort implementation of kalman filtering with spiking neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695172/
https://www.ncbi.nlm.nih.gov/pubmed/36433442
http://dx.doi.org/10.3390/s22228845
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