Abstract Through System Identification techniques, it is possible to obtain a mathematical
model for a dynamic system from its input/output data. Due to their intrinsic dynamic …

Quantum-Inspired Evolutionary Algorithms (QIEA) represent an efficient alternative to the
traditional genetic algorithms, being capable of finding good solutions with smaller …

Echo state networks (ESNs) have been shown to be an effective alternative to conventional
recurrent neural networks due to its simple training process and good fitting performance of …

Echo state network (ESN) is a powerful tool for nonlinear system modeling. However, the
random setting of structure (mainly the reservoir) may degrade its estimation accuracy. To …

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 …

Abstract Echo State Networks (ESNs) are widely-used Recurrent Neural Networks. They are
dynamical systems including, in state-space form, a nonlinear state equation and a linear …

In this paper, a new identification method based on echo state network (ESN) is proposed to
identify the parameters of a class of discrete-time nonlinear systems. Through analyzing the …

Aiming to solve the problem of selecting echo state network parameters, a method for
optimizing echo state network (ESN) parameters based on the behavior space is proposed …

The present work introduces a procedure for input selection and parameter estimation for
system identification based on Radial Basis Functions Neural Networks (RBFNNs) models …

As extension of traditional echo state networks (ESNs), the polynomial echo state networks
(PESNs) have been proposed in our previous work (Yang et al., 208) by employing the …