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 …

We propose photonic reservoir computing as a new approach to optical signal processing in
the context of large scale pattern recognition problems. Photonic reservoir computing is a …

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 …

A new class of state-space models, reservoir models, with a fixed state transition structure
(the “reservoir”) and an adaptable readout from the state space, has recently emerged as a …

Prediction of multi-dimensional time-series data, which may represent such diverse
phenomena as climate changes or financial markets, remains a challenging task in view of …

In the last twenty years, Echo State Networks have become a prominent method for the
prediction of time series with a large variety of proposed topologies of connections. These …

There is a growing understanding that machine learning architectures have to be much
bigger and more complex to approach any intelligent behavior. There is also a growing …

In this study, a novel approach that is based on reservoir computing, which is a successful
method in modeling sequential datasets, and extreme learning machines, which has a high …

A novel self-learning optimal control method for a class of discrete-time nonlinear systems is
proposed based on iteration adaptive dynamic programming (ADP) algorithm. It is proven …

A lot of attention is now being focused on connectionist models known under the name
“reservoir computing”. The most prominent example of these approaches is a recurrent …