Through the success of artificial neural networks (ANNs) in different domains, intense
research has been recently centered on changing the networks architecture to optimize the …

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 …

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 …

Cortical neural connectivity has been shown to exhibit a small-world (SW) network topology.
However, the role of the topology in neural information processing remains unclear. In this …

Wind turbines are among the most popular sources of renewable energy. The energy
available from wind varies widely because wind energy is highly dependent on continually …

In this paper, we propose a novel computational architecture of memristor-based echo state
network (MESN) with the online least mean square (LMS) algorithm. Newman and Watts …

Self-assembled networks of nanoparticles and nanowires have recently emerged as
promising systems for brain-like computation. Here, we focus on percolating networks of …

Deep belief network (DBN) has attracted many attentions in time series prediction. However,
the DBN-based methods fail to provide favorable prediction results due to the congenital …

In this paper, energy consumption of an office building is predicted based on echo state
networks (ESNs). Energy consumption of the office building is divided into consumptions …

The echo state network (ESN) is a novel and powerful method for the temporal processing of
recurrent neural networks. It has tremendous potential for solving a variety of problems …