Physical reservoir computing with emerging electronics
Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …
properties of materials for high-efficiency computing. A wide range of physical systems can …
Adaptive echo state network with a recursive inverse-free weight update algorithm
B Wang, S Lun, M Li, X Lu, T Tao - Information Sciences, 2023 - Elsevier
Abstract Echo State Network (ESN) is widely applied in sequence prediction, physics, and
economics. Moore-Penrose inversion is a typical solving process of ESN. However, in the …
economics. Moore-Penrose inversion is a typical solving process of ESN. However, in the …
Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks
G Hu, RW Whalin, TA Kwembe, W Lu - Physica A: Statistical Mechanics and …, 2023 - Elsevier
Short-term traffic flow prediction is a significant and challenging research topic as it is closely
related to the application of intelligent transportation systems. Due to the variable and …
related to the application of intelligent transportation systems. Due to the variable and …
Reservoir computing for drone trajectory intent prediction: a physics informed approach
A Perrusquía, W Guo - IEEE Transactions on Cybernetics, 2024 - ieeexplore.ieee.org
The design of accurate trajectory prediction algorithms is crucial to implement adequate
countermeasures against drones with anomalous performances. Wrong predictions may …
countermeasures against drones with anomalous performances. Wrong predictions may …
Uncovering drone intentions using control physics informed machine learning
Abstract Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across
diverse application areas. Uncooperative drones do not announce their identity/flight plans …
diverse application areas. Uncooperative drones do not announce their identity/flight plans …
Learning reservoir dynamics with temporal self-modulation
Y Sakemi, S Nobukawa, T Matsuki, T Morie… - Communications …, 2024 - nature.com
Reservoir computing (RC) can efficiently process time-series data by mapping the input
signal into a high-dimensional space via randomly connected recurrent neural networks …
signal into a high-dimensional space via randomly connected recurrent neural networks …
Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks
The echo state network (ESN) is an excellent machine learning model for processing time-
series data. This model, utilising the response of a recurrent neural network, called a …
series data. This model, utilising the response of a recurrent neural network, called a …
Microwave signal processing using an analog quantum reservoir computer
Quantum reservoir computing (QRC) has been proposed as a paradigm for performing
machine learning with quantum processors where the training is efficient in the number of …
machine learning with quantum processors where the training is efficient in the number of …
Deep echo state networks for detecting internet worm and ransomware attacks
With the advancement of technology over the last decade, there has been a rapid increase
in the number and types of malware attacks such as worms whose primary function is to self …
in the number and types of malware attacks such as worms whose primary function is to self …
On the prediction of the turbulent flow behind cylinder arrays via Echo State Networks
M Sharifi Ghazijahani, C Cierpka - Machine Learning: Science …, 2024 - iopscience.iop.org
This study aims at the prediction of the turbulent flow behind cylinder arrays by the
application of Echo State Networks (ESN). Three different arrangements of arrays of seven …
application of Echo State Networks (ESN). Three different arrangements of arrays of seven …