Hybrid neural network-particle swarm algorithm to describe chaotic time series

Title:

Hybrid neural network-particle swarm algorithm to describe chaotic time series

Creator:

Lazzús, Juan A., Salfate, Ignacio, and Montecinos, Sonia

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:e660f8bf-7000-4a3d-8eff-00d81796e3ab
uuid:e660f8bf-7000-4a3d-8eff-00d81796e3ab
doi:10.14311/NNW.2014.24.034

Subject:

Chaotic time series, time series prediction, Mackey-Glass series, artificial neural network, and particle swarm optimization

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. This hybrid ANN+PSO algorithm was applied on Mackey-Glass series in the short-term prediction x(t+6) and the long-term prediction x(t+84), from the current value x(t) and the past values: x(t-6), x(t-12), x(t-18). Four cases were studied, alternating the timedelay parameter as 17 or 30. Also, the first four largest Lyapunov exponents were obtained for different time-delay. Simulation shows that this ANN+PSO method is a very powerful tool for making prediction of chaotic time series.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2014 Volume:24 | Number:6

Harvested from:

CDK

Metadata only:

false