It's all delightfully absurd, echoing "Network" and its heirs.
Different from the traditional fully connected recurrent neural networks, an echo state network (ESN) adopts the sparsely connected hidden layer in which the connectivity and weights of hidden neuron nodes are fixed and randomly assigned.
Then echo state network, a special kind of recurrent neural network, is used to predict the pedestrian count from the input pattern.
The echo state network with CLF inherits the basic architecture of echo state network, but replaces the commonly used mean square error (MSE) criterion with CLF.
In this study, a granular echo state network (ESN) is developed for PIs construction, in which the network connections are represented by the interval-valued information granules.
Aimed to enhance the supporting ability for diversified services, this paper proposes a hierarchy echo state network (HESE) based service-awareness (SA) (HESN-SA) mechanism in 10 Gbit/s Ethernet passive optical network (10G-EPON).
The echo state network (ESN) is a novel and powerful method for the temporal processing of recurrent neural networks.
The echo state network was very powerful in such respects.
In this paper, a robust echo state network with correntropy induced loss function (CLF) is presented.
Fast convergence rate and low computational complexity features are important issues for high data rate applications such as speech processing, echo cancelation, network echo cancelation, and channel equalization.
This paper proposes a novel echo state network (ESN) architecture in a deep learning framework for time series prediction.
