MFRFNN: Multi-functional recurrent fuzzy neural network for chaotic time series prediction
Chaotic time series prediction, a challenging research topic in dynamic system modeling,
has drawn great attention from researchers around the world. In recent years extensive …
has drawn great attention from researchers around the world. In recent years extensive …
Learning from the past: reservoir computing using delayed variables
Reservoir computing is a machine learning method that is closely linked to dynamical
systems theory. This connection is highlighted in a brief introduction to the general concept …
systems theory. This connection is highlighted in a brief introduction to the general concept …
Nonlinear spiking neural systems with autapses for predicting chaotic time series
Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing
models that are inspired by the mechanism of spiking neurons and are 3rd-generation …
models that are inspired by the mechanism of spiking neurons and are 3rd-generation …
Growing echo-state network with multiple subreservoirs
J Qiao, F Li, H Han, W Li - IEEE transactions on neural …, 2016 - ieeexplore.ieee.org
An echo-state network (ESN) is an effective alternative to gradient methods for training
recurrent neural network. However, it is difficult to determine the structure (mainly the …
recurrent neural network. However, it is difficult to determine the structure (mainly the …
Multivariate time series forecasting method based on nonlinear spiking neural P systems and non-subsampled shearlet transform
Multivariate time series forecasting remains a challenging task because of its nonlinear, non-
stationary, high-dimensional, and spatial–temporal characteristics, along with the …
stationary, high-dimensional, and spatial–temporal characteristics, along with the …
Robust echo state network with Cauchy loss function and hybrid regularization for noisy time series prediction
F Li, Y Li - Applied Soft Computing, 2023 - Elsevier
Noisy time series prediction is a hot research topic in practical applications. Echo state
networks (ESNs) have superior performance on time series prediction. However, the ill …
networks (ESNs) have superior performance on time series prediction. However, the ill …
Adaptive elastic echo state network for multivariate time series prediction
Echo state network (ESN) is a new kind of recurrent neural network with a randomly
generated reservoir structure and an adaptable linear readout layer. It has been widely …
generated reservoir structure and an adaptable linear readout layer. It has been widely …
Optimizing the echo state network with a binary particle swarm optimization algorithm
H Wang, X Yan - Knowledge-Based Systems, 2015 - Elsevier
The echo state network (ESN) is a novel and powerful method for the temporal processing of
recurrent neural networks. It has tremendous potential for solving a variety of problems …
recurrent neural networks. It has tremendous potential for solving a variety of problems …
Reservoir computing with delayed input for fast and easy optimisation
Reservoir computing is a machine learning method that solves tasks using the response of a
dynamical system to a certain input. As the training scheme only involves optimising the …
dynamical system to a certain input. As the training scheme only involves optimising the …
Simple deterministically constructed cycle reservoirs with regular jumps
A new class of state-space models, reservoir models, with a fixed state transition structure
(the “reservoir”) and an adaptable readout from the state space, has recently emerged as a …
(the “reservoir”) and an adaptable readout from the state space, has recently emerged as a …