scholarly journals Learning Algorithm for Fractional Dynamical Systems with Autocorrelated Errors-in-Variables

2019 ◽  
Vol 154 ◽  
pp. 311-318
Author(s):  
Dmitriy V. Ivanov ◽  
Ilya L. Sandler ◽  
Natalya V. Chertykovtseva ◽  
Elena U. Bobkova
Keyword(s):  
Dynamical Systems ◽  
Learning Algorithm ◽  
Errors In Variables ◽  
Autocorrelated Errors

Linear dynamic errors-in-variables models

1986 ◽  
Vol 23 (A) ◽  
pp. 23-39 ◽  
Author(s):  
M. Deistler
Keyword(s):  
Dynamical Systems ◽  
Transfer Functions ◽  
Errors In Variables ◽  
Linear Dynamical Systems ◽  
Linear Dynamic ◽  
Dynamic Errors ◽  
Non Gaussian ◽  
Inputs And Outputs

Linear dynamical systems where both inputs and outputs are contaminated by errors are considered. A characterization of the sets of all observationally equivalent transfer functions is given, the role of the causality assumption is investigated and conditions for identifiability in the case of Gaussian as well as non-Gaussian observations are derived.

scholarly journals Photonic reservoir computing using delay dynamical systems

Photoniques ◽  
2020 ◽  
pp. 45-48
Author(s):  
Piotr Antonik ◽  
Serge Massar ◽  
Guy Van Der Sande
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Dynamical Systems ◽  
Recent Progress ◽  
Learning Algorithm ◽  
Reservoir Computing ◽  
Machine Learning Algorithm ◽  
Hardware Implementations ◽  
All Optical

The recent progress in artificial intelligence has spurred renewed interest in hardware implementations of neural networks. Reservoir computing is a powerful, highly versatile machine learning algorithm well suited for experimental implementations. The simplest highperformance architecture is based on delay dynamical systems. We illustrate its power through a series of photonic examples, including the first all optical reservoir computer and reservoir computers based on lasers with delayed feedback. We also show how reservoirs can be used to emulate dynamical systems. We discuss the perspectives of photonic reservoir computing.

Linear dynamic errors-in-variables models

1986 ◽  
Vol 23 (A) ◽  
pp. 23-39 ◽  
Author(s):  
M. Deistler
Keyword(s):  
Dynamical Systems ◽  
Transfer Functions ◽  
Errors In Variables ◽  
Linear Dynamical Systems ◽  
Linear Dynamic ◽  
Dynamic Errors ◽  
Non Gaussian ◽  
Inputs And Outputs

Linear dynamical systems where both inputs and outputs are contaminated by errors are considered. A characterization of the sets of all observationally equivalent transfer functions is given, the role of the causality assumption is investigated and conditions for identifiability in the case of Gaussian as well as non-Gaussian observations are derived.

Sign in / Sign up

Export Citation Format

Share Document