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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9695172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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