Time-series prediction is a popular application that relies on the collection of historical data
via sensors, which is then leveraged by predictive models to forecast future values or trends …

This paper presents a new hybrid neural network controller for time series prediction and
chaotic synchronization. The proposed controller is called as a wavelet interval type-2 …

Reservoir computing has shown promising results in predicting chaotic time series.
However, the main challenges of time-series predictions are associated with reducing …

Abstract Echo State Network (ESN) is an effective variant of Recurrent Neural Network
(RNN). However, it is difficult for traditional ESN to determine the reservoir size that matches …

Abstract The Echo State Network (ESN) is one of the most used machine learning methods
for predicting chaotic time series. The topology of an ESN comprises three layers, namely …

As artificial intelligence (AI) and machine learning continue to advance, they have become
key players in signal analysis and pattern recognition. However, challenges such as domain …

Echo state network (ESN) has gained wide acceptance in the field of time series prediction,
relying on sufficiently complex reservoir connections to remember the historical features of …

Reservoir computing is a machine learning framework where the readouts from a nonlinear
system (the reservoir) are trained so that the output from the reservoir, when forced with an …

Continuous Time Echo State Networks (CTESNs) are a promising yet under-explored
surrogate modeling technique for dynamical systems, particularly those governed by stiff …

This paper introduces a novel approach called Chebyshev mapping and strongly connected
topology for optimization of echo state network (ESN). To enhance the predictive …