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 …

This book is devoted to a comprehensive compilation of the first hardware platforms
employed for photonic reservoir computing. Reservoir computing is a machine learning …

Abstract From one side, Evolutionary Algorithms have enabled enormous progress over the
last years in the optimization field. They have been applied to a variety of problems …

Reservoir computing is a bioinspired computing paradigm for processing time-dependent
signals. Its hardware implementations have received much attention because of their …

Abstract Machine learning provides fundamental tools both for scientific research and for the
development of technologies with significant impact on society. It provides methods that …

Motivated by the idea of'decomposition and ensemble', this paper proposes a novel method
based on the wavelet-denoising algorithm and multiple echo state networks to improve the …

Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers
are expanded and recoded to provide a foundation from which the Purkinje cells can …

Echo state networks (ESNs) have wide applications in chaotic time series prediction. In the
ESN, if the smallest singular value of the reservoir state matrix is infinitesimal, the ill-posed …

Echo state network (ESN), a novel recurrent neural network, has a randomly and sparsely
connected reservoir. Since the reservoir size is very large, the collinearity problem may exist …

A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose
performance often depends on sensitive hyperparameters. Tuning them properly may be …