摘要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.
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.
Juan A. Lazzús**
. Predicting Natural and Chaotic Time Series with a Swarm-Optimized Neural Network[J]. 中国物理快报, 2011, 28(11): 110504-110504.
Juan A. Lazzús**
. Predicting Natural and Chaotic Time Series with a Swarm-Optimized Neural Network. Chin. Phys. Lett., 2011, 28(11): 110504-110504.
[1] Han M and Wang Y 2009 Exp. Syst. Appl. 36 1280
[2] Bezruchko B P et al 2001 Phys. Rev. E 64 056216
[3] Farmer J D and Sidorowich J J 1987 Phys. Rev. Lett. 59 845
[4] Zhang J S and Xiao X C 2000 Chin. Phys. Lett. 17 88
[5] Wong F S 1991 Neurocomputing 2 147
[6] Chng E S et al 1996 IEEE Trans. Neural Networks 7 190
[7] Kim D and Kim C 1997 IEEE Trans. Fuzzy Systems 5 523
[8] Jang J S R et al 1997 Neuro-fuzzy and Soft Computing (New Jersey: Prentice-Hall)
[9] Lin D, Wang X 2011 Neurocomputing 74 2241
[10] Fu Y Y et al 2010 Exp. Syst. Appl. 37 4441
[11] Cho K B and Wang B H 1996 Fuzzy Sets Syst. 83 325
[12] Chen Y et al 2006 Neurocomputing 69 449
[13] Geethanjali M et al 2008 Neurocomputing 71 904
[14] Lazzús J A 2010 Comput. Math. Appl. 60 2260
[15] Kennedy J and Eberhart R 1995 IEEE Int. Conf. Neural Networks 4 1942
[16] Lazzús J A 2009 Int. J. Thermophys. 30 883
[17] Mackey M C and Glass L 1977 Science 197 287
[18] Farmer J D 1982 Physica D 4 336
[19] Mohandes M et al 1998 Renew. Energy 14 179
[20] Sözen A et al 2004 Energy Convers. Manag. 45 3033
[21] Rehman S and Mohandes M 2008 Energy Policy 36 571
[22] Lazzús J A et al 2011 Appl. Solar Energy 47 66
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