TY - JOUR
T1 - Implementation of Kalman Filtering with Spiking Neural Networks
AU - Juárez-Lora, Alejandro
AU - García-Sebastián, Luis M.
AU - Ponce-Ponce, Victor H.
AU - Rubio-Espino, Elsa
AU - Molina-Lozano, Herón
AU - Sossa, Humberto
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - Kalman filter
KW - artificial intelligence
KW - dynamics
KW - robotics
KW - spiking neural networks
UR - http://www.scopus.com/inward/record.url?scp=85142681041&partnerID=8YFLogxK
U2 - 10.3390/s22228845
DO - 10.3390/s22228845
M3 - Artículo
C2 - 36433442
AN - SCOPUS:85142681041
SN - 1424-8220
VL - 22
JO - Sensors
JF - Sensors
IS - 22
M1 - 8845
ER -