Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network

Computational Intelligence and Neuroscience
C H López-CaraballoL Palma-Chilla

Abstract

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.

References

Jul 15, 1977·Science·M C Mackey, L Glass
Dec 12, 2001·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·B P BezruchkoM D Prokhorov
Sep 21, 2004·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Chia-Feng Juang
Mar 28, 2007·IEEE Transactions on Neural Networks·Ying SongZhuzhi Yuan
Feb 6, 2008·IEEE Transactions on Neural Networks·H LeungS Wang
Jan 1, 1996·IEEE Transactions on Neural Networks·E S ChngB Mulgrew
May 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Decai LiJun Wang

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Software Mentioned

PSO
ANN

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