Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network.
An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σ(N)) from 0.01 to 0.1.
Departamento de Física y Astronomía, Universidad de La Serena, Casilla 554, La Serena, Chile.
C H
CH
López-Caraballo
Departamento de Física y Astronomía, Universidad de La Serena, Casilla 554, La Serena, Chile.
J A
JA
Lazzús
Departamento de Física y Astronomía, Universidad de La Serena, Casilla 554, La Serena, Chile.
I
I
Salfate
Departamento de Física y Astronomía, Universidad de La Serena, Casilla 554, La Serena, Chile.
P
P
Rojas
Departamento de Física y Astronomía, Universidad de La Serena, Casilla 554, La Serena, Chile.
M
M
Rivera
Departamento de Física y Astronomía, Universidad de La Serena, Casilla 554, La Serena, Chile.
L
L
Palma-Chilla
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005544:Forecasting / Q000379:methods*; D016571:Neural Networks, Computer; D013997:Time Factors; D035501:Uncertainty