In this paper, an intelligent technique for EMS based on Recurrent Neural Network (RNN)
with aid of Ant-Lion Optimizer (ALO) algorithm is presented to find energy scheduling in MG …

A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is
presented. RC is understood in the literature as the possibility of approximating input–output …

Reservoir Computing (RC) is an umbrella term for adaptive computational paradigms that
rely on an excitable dynamical system, also called the" reservoir." The paradigms have been …

In recent years, the artificial intelligence community has seen a continuous interest in
research aimed at investigating dynamical aspects of both training procedures and machine …

Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in
the literature often contradict well-established theoretical bounds. In this paper, we study the …

The notion of memory capacity, originally introduced for echo state and linear networks with
independent inputs, is generalized to nonlinear recurrent networks with stationary but …

Reservoir computing provides a time and cost-efficient alternative to traditional learning
methods. Critical regimes, known as the “edge of chaos,” have been found to optimize …

Macroeconomic forecasting has recently started embracing techniques that can deal with
large-scale datasets and series with unequal release periods. Mixed-data sampling (MIDAS) …

An optimal utilization of smart grid connected hybrid renewable energy sources is proposed
in this paper. The hybrid technique is the combination of recurrent neural network and …

Recurrent networks are trained to memorize their input better, often in the hopes that such
training will increase the ability of the network to predict. We show that networks designed to …