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 …

Reservoir computing has emerged in the last decade as an alternative to gradient descent
methods for training recurrent neural networks. Echo State Network (ESN) is one of the key …

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for
processing time dependent data. The basic scheme of reservoir computing consists of a non …

Reservoir computing is a brain-inspired machine learning framework that employs a signal-
driven dynamical system, in particular harnessing common-signal-induced synchronization …

Sensors are the primary information source of the aeroengine control system, their
measurement accuracy is closely related to whether the engine can operate safely and …

Echo State Networks (ESNs) constitute an emerging approach for efficiently modeling
Recurrent Neural Networks (RNNs). In this paper we investigate some of the main aspects …

Random projection architectures such as Echo state networks (ESNs) and Extreme Learning
Machines (ELMs) use a network containing a randomly connected hidden layer and train …

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-
like dynamics arising from the interplay of their synapse-like electrical junctions and their …

Deep neural networks (DNNs) have widely used in various Internet-of-Things (IoT)
applications. Pursuing superior performance is always a hot spot in the field of DNN …

The recognition of human actions in videos is one of the most active research fields in
computer vision. The canonical approach consists in a more or less complex preprocessing …