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 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 …

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …

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 …

Classification of multivariate time series (MTS) has been tackled with a large variety of
methodologies and applied to a wide range of scenarios. Reservoir computing (RC) …

In this paper, a robust recurrent neural network is presented in a Bayesian framework based
on echo state mechanisms. Since the new model is capable of handling outliers in the …

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 …

In this paper, we have described a solution for supporting independent living of the elderly
by means of equipping their home with a simple sensor network to monitor their behaviour …

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 …