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Predicting Natural and Chaotic Time Series with a Swarm-Optimized Neural Network |
| Juan A. Lazzús**
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Departamento de Física, Universidad de La Serena, Casilla 554, La Serena, Chile
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| Cite this article: |
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Juan A. Lazzús 2011 Chin. Phys. Lett. 28 110504 |
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Abstract Natural and chaotic time series are predicted using an artificial neural network (ANN) based on particle swarm optimization (PSO). Firstly, the hybrid ANN+PSO algorithm is applied on Mackey–Glass series in the short-term prediction x(t+6), using the current value x(t) and the past values: x(t−6), x(t−12), x(t−18). Then, this method is applied on solar radiation data using the values of the past years: x(t−1), ..., x(t−4). The results show that the ANN+PSO method is a very powerful tool for making predictions of natural and chaotic time series.
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| Keywords:
05.45.+b
05.45.Tp
05.45.Pq
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Received: 31 May 2011
Published: 30 October 2011
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