Results 21 to 30 of about 233,583 (184)

Exploiting Multiple Timescales in Hierarchical Echo State Networks

Frontiers in Applied Mathematics and Statistics, 2021
Echo state networks (ESNs) are a powerful form of reservoir computing that only require training of linear output weights while the internal reservoir is formed of fixed randomly connected neurons. With a correctly scaled connectivity matrix, the neurons’
Luca Manneschi, Matthew O. A. Ellis, Guido Gigante, Andrew C. Lin, Andrew C. Lin, Paolo Del Giudice, Eleni Vasilaki   +6 more
doaj   +1 more source

Drowsiness Estimation of Drivers Using Echo State Networks

International Journal of Automotive Engineering, 2022
Auto-estimation of drowsiness of a vehicle driver is an urgent problem, as more than 15% of traffic accidents are caused by careless driving in Japan. This paper considers drowsiness estimation based on the measurements of the heartbeat, respiration, and
Ryo Ariizumi, Masanori Kawaguchi, Toshiya Arakawa, Naoya Oue, Masaru Murayama   +4 more
doaj   +1 more source

A novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks

BMC Medical Informatics and Decision Making, 2010
Background Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data.
De Turck F, Benoit D, Steurbaut K, Van Looy S, Verplancke T, De Moor G, Decruyenaere J   +6 more
doaj   +1 more source

Symbiotic Ocean Modeling Using Physics‐Controlled Echo State Networks

Journal of Advances in Modeling Earth Systems, 2023
We introduce a “symbiotic” ocean modeling strategy that leverages data‐driven and machine learning methods to allow high‐ and low‐resolution dynamical models to mutually benefit from each other.
T. E. Mulder, S. Baars, F. W. Wubs, F. I. Pelupessy, M. Verstraaten, H. A. Dijkstra   +5 more
doaj   +1 more source

Fast and adaptive dynamics-on-graphs to dynamics-of-graphs translation

Frontiers in Big Data, 2023
Numerous networks in the real world change with time, producing dynamic graphs such as human mobility networks and brain networks. Typically, the “dynamics on graphs” (e.g., changing node attribute values) are visible, and they may be connected to and ...
Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao   +3 more
doaj   +1 more source

Tree Echo State Networks [PDF]

Neurocomputing, 2013
In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained generalized recursive reservoir, exhibiting extreme efficiency for learning in structured domains. In addition, we highlight through the paper other characteristics of the approach:
GALLICCHIO, CLAUDIO, MICHELI, ALESSIO
openaire   +2 more sources

The spectral radius remains a valid indicator of the echo state property for large reservoirs [PDF]

, 2013
In the field of Reservoir Computing, scaling the spectral radius of the weight matrix of a random recurrent neural network to below unity is a commonly used method to ensure the Echo State Property.
Caluwaerts, Ken, Dieleman, Sander, Schrauwen, Benjamin, wyffels, Francis   +3 more
core   +1 more source

Nonlinear system modeling with random matrices: echo state networks revisited. [PDF]

IEEE Trans Neural Netw Learn Syst, 2012
Zhang B, Miller DJ, Wang Y.
europepmc   +2 more sources

Research on predictive control of energy saving for central heating based on echo state network

Energy Reports, 2023
It is very important for energy conservation and environmental protection to realize energy-saving control of central heating system by prediction method.
Li Liu, Jun Liu, Zhifeng Chen, Zhongyang Jiang, Ming Pang, Yanshu Miao   +5 more
doaj   +1 more source

Musical instrument mapping design with Echo State Networks [PDF]

, 2014
Echo State Networks (ESNs), a form of recurrent neural network developed in the field of Reservoir Computing, show significant potential for use as a tool in the design of mappings for digital musical instruments.
Kiefer, Chris
core   +2 more sources