Universal Discrete-Time Reservoir Computers with Stochastic Inputs and Linear Readouts Using Non-Homogeneous State-Affine Systems
Abstract
A new class of non-homogeneous state-affine systems is introduced for use in reservoir computing. Sufficient conditions are identified that guarantee first, that the associated reservoir computers with linear readouts are causal, time-invariant, and satisfy the fading memory property and second, that a subset of this class is universal in the category of fading memory filters with stochastic almost surely uniformly bounded inputs. This means that any discrete-time filter that satisfies the fading memory property with random inputs of that type can be uniformly approximated by elements in the non-homogeneous state-affine family.
Cite
Text
Grigoryeva and Ortega. "Universal Discrete-Time Reservoir Computers with Stochastic Inputs and Linear Readouts Using Non-Homogeneous State-Affine Systems." Journal of Machine Learning Research, 2018.Markdown
[Grigoryeva and Ortega. "Universal Discrete-Time Reservoir Computers with Stochastic Inputs and Linear Readouts Using Non-Homogeneous State-Affine Systems." Journal of Machine Learning Research, 2018.](https://mlanthology.org/jmlr/2018/grigoryeva2018jmlr-universal/)BibTeX
@article{grigoryeva2018jmlr-universal,
title = {{Universal Discrete-Time Reservoir Computers with Stochastic Inputs and Linear Readouts Using Non-Homogeneous State-Affine Systems}},
author = {Grigoryeva, Lyudmila and Ortega, Juan-Pablo},
journal = {Journal of Machine Learning Research},
year = {2018},
pages = {1-40},
volume = {19},
url = {https://mlanthology.org/jmlr/2018/grigoryeva2018jmlr-universal/}
}