Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

Echo State Networks and Liquid State Machines introduced a new paradigm in artificial
recurrent neural network (RNN) training, where an RNN (the reservoir) is generated …

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

In today's age, companies employ machine learning to extract information from large
quantities of data. One of those techniques, reservoir computing (RC), is a decade old and …

Wind speed forecasting is a promising solution to improve the efficiency of energy utilization.
In this study, a novel hybrid wind speed forecasting model is proposed. The whole modeling …

The key component in forecasting demand and consumption of resources in a supply
network is an accurate prediction of real-valued time series. Indeed, both service …

A conspicuous ability of the brain is to seamlessly assimilate and process spatial and
temporal features of sensory stimuli. This ability is indispensable for the recognition of …

The connection patterns of neural circuits in the brain form a complex network. Collective
signalling within the network manifests as patterned neural activity and is thought to support …

Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural
Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the …

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 …