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 …

In recent years, the explosive development of artificial intelligence implementing by artificial
neural networks (ANNs) creates inconceivable demands for computing hardware. However …

In this paper, we provide a novel approach to the architectural design of deep Recurrent
Neural Networks using signal frequency analysis. In particular, focusing on the Reservoir …

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 …

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 …

Reservoir computing (RC) refers to a new class of state-space models with a fixed state
transition structure (the reservoir) and an adaptable readout form the state space. The …

Training recurrent neural networks is hard. Recently it has however been discovered that it
is possible to just construct a random recurrent topology, and only train a single linear …

Introduced a decade ago, reservoir computing is an efficient approach for signal processing.
State of the art capabilities have already been demonstrated with both computer simulations …

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 …

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 …