Echo state networks (ESNs) are randomly connected recurrent neural networks (RNNs) that
can be used as a temporal kernel for modeling time series data, and have been successfully …

Echo state networks (ESNs) are a new approach to recurrent neural networks (RNNs) that
have been successfully applied in many domains. Nevertheless, an ESN is a predictive …

Abstract An echo State Network (ESN) is a special structure of a recurrent neural network
(RNN) in which the recurrent neurons are randomly connected. ESN models which have …

Abstract Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs) which
have been successfully applied to time series modeling tasks. However, ESNs are unable to …

Recently, ESN has been applied to time series classification own to its high-dimensional
random projection ability and training efficiency characteristic. The major drawback of …

Echo state networks (ESNs) belong to the class of recurrent neural networks and have
demonstrated robust performance in time series prediction tasks. In this study, we …

Learning in the space of Echo State Network (ESN) output weights, ie model space, has
achieved excellent results in time series classification, visualization and modelling. This …

For many years, neural networks have gained gigantic interest and their popularity is likely
to continue because of the success stories of deep learning. Nonetheless, their applications …

As efficient recurrent neural network (RNN) models, echo state networks (ESNs) have
attracted widespread attention and been applied in many application domains in the last …

Time series classification is an active research field and applicable in many domains, eg
speech and gesture recognition. A recent approach to classify time series is based on …