Forecasting sunspot numbers with the aid of fuzzy descriptor models
The cyclic solar activity has significant effects on Earth, satellites, and space missions. The prediction of sunspot number is an active research area and several methods have been introduced for its prediction, which is a common measure of solar activity. On the other hand, descriptor models and r...
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| Vydáno v: | Space weather Ročník 5; číslo 8; s. np - n/a |
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Blackwell Publishing Ltd
01.08.2007
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| ISSN: | 1542-7390, 1542-7390 |
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| Abstract | The cyclic solar activity has significant effects on Earth, satellites, and space missions. The prediction of sunspot number is an active research area and several methods have been introduced for its prediction, which is a common measure of solar activity. On the other hand, descriptor models and related fuzzy descriptor models have been the subjects of interest due to their many practical applications in modeling complex phenomena. In this study, it is tried to predict sun spot number by a data driven approach. In other words, instead of other methods which are based on sophisticated models, in this paper a fuzzy descriptor model is used as a black box to predict sunspot number. To do so, a novel learning method, generalized locally linear model tree (GLOLIMOT) algorithm for fuzzy descriptor models as an intuitive incremental learning algorithms, is introduced to tune the parameters of fuzzy descriptor model for the prediction of sunspot number via empirical data. The contribution of this paper is to provide some methods for adjusting the parameters of fuzzy descriptor model, e.g., the splitting ratio and the standard deviation, the number of locally linear neurons and the number of linear descriptor systems for the consequent part in fuzzy descriptor model and especially the parameters of such descriptor systems which need some special methods for these systems. By these modifications an accurate prediction of sunspot number is obtained which, when compared with several methods and results, depict the power of these systems in predicting such complex phenomena. |
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| AbstractList | The cyclic solar activity has significant effects on Earth, satellites, and space missions. The prediction of sunspot number is an active research area and several methods have been introduced for its prediction, which is a common measure of solar activity. On the other hand, descriptor models and related fuzzy descriptor models have been the subjects of interest due to their many practical applications in modeling complex phenomena. In this study, it is tried to predict sun spot number by a data driven approach. In other words, instead of other methods which are based on sophisticated models, in this paper a fuzzy descriptor model is used as a black box to predict sunspot number. To do so, a novel learning method, generalized locally linear model tree (GLOLIMOT) algorithm for fuzzy descriptor models as an intuitive incremental learning algorithms, is introduced to tune the parameters of fuzzy descriptor model for the prediction of sunspot number via empirical data. The contribution of this paper is to provide some methods for adjusting the parameters of fuzzy descriptor model, e.g., the splitting ratio and the standard deviation, the number of locally linear neurons and the number of linear descriptor systems for the consequent part in fuzzy descriptor model and especially the parameters of such descriptor systems which need some special methods for these systems. By these modifications an accurate prediction of sunspot number is obtained which, when compared with several methods and results, depict the power of these systems in predicting such complex phenomena. |
| Author | Lucas, Caro Mirmomeni, Masoud Shafiee, Masoud Araabi, Babak Nadjar |
| Author_xml | – sequence: 1 givenname: Masoud surname: Mirmomeni fullname: Mirmomeni, Masoud email: m.mirmomeni@ece.ut.ac.ir organization: Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran – sequence: 2 givenname: Caro surname: Lucas fullname: Lucas, Caro organization: Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran – sequence: 3 givenname: Babak Nadjar surname: Araabi fullname: Araabi, Babak Nadjar organization: Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran – sequence: 4 givenname: Masoud surname: Shafiee fullname: Shafiee, Masoud organization: Electrical Engineering Department, University of Amirkabir, Tehran, Iran |
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| CitedBy_id | crossref_primary_10_1134_S0016793221080065 crossref_primary_10_1016_j_newast_2018_01_009 crossref_primary_10_1016_j_jastp_2015_08_010 crossref_primary_10_1007_s12591_022_00615_w crossref_primary_10_1016_j_asr_2011_03_037 crossref_primary_10_1016_j_asr_2013_04_026 |
| Cites_doi | 10.1109/TSMCB.2003.817053 10.1109/TPS.2004.833398 10.1109/PROC.1973.9023 10.1109/91.868950 10.1029/2005GL025221 10.1088/1009-9271/2/6/557 10.1109/ICSMC.2003.1244358 10.1029/2005SW000209 10.1049/ic:19970788 10.1162/neco.1993.5.2.305 10.1109/TSMC.1985.6313399 10.1007/BFb0002475 10.1109/CDC.2004.1430349 10.1023/A:1005087612053 10.1007/978-3-662-04323-3 10.1142/S0129065790000102 10.1109/TAC.1977.1101502 10.1007/BFb0052854 10.1016/S0277-3791(99)00072-4 10.1109/CDC.2004.1429291 10.1111/j.2517-6161.1992.tb01884.x 10.1109/TAC.1981.1102763 10.1109/IJCNN.2006.247388 10.1016/j.jastp.2004.12.001 10.1007/s00521‐006‐0062‐x 10.1111/j.2517-6161.1980.tb01126.x 10.1007/BF03023002 10.1109/TAC.2005.864196 |
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| References_xml | – reference: Luenberger, D. G. (1977), Dynamic equations in descriptor form, IEEE Trans. Auto. Control, AC-22, 312-321. – reference: Lantos, P., and O. Richard (1998), On the prediction of maximum amplitude for solar cycles using geomagnetic precursors, Sol. Phys., 182, 231-246. – reference: Park, J., and I. W. Sandberg (1993), Approximation and radial basis function networks, Neural Comput., 5, 305-316. – reference: Beer, J., W. Mende, and R. Stellmacher (2000), The role of the sun in climate forcing, Quat. Sci. Rev., 19, 403-415. – reference: Wang, J.-L., J.-C. Gong, S.-Q. Liu, G.-M. Le, and J.-L. Sun (2002), The prediction of maximum amplitudes of solar cycles and the maximum amplitude of solar cycle 24, Chin. J. Astron. Astrophys., 2, 557-562. – reference: Takagi, T., and M. Sugeno (1985), Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man Cybern., SMC-15, 116-132. – reference: Nelles, O. (2001), Nonlinear System Identification, Springer-Verlag, Berlin. – reference: Izeman, A. J. (1985), Wolf J. R. and the Zurich sunspot relative numbers, Math. Intell., 7(1), 27-33. – reference: Klawon, F., and P. E. Klement (1997), Mathematical analysis of fuzzy classifiers, Lect. Notes Comput. Sci., 1280, 359-370. – reference: Quassim, M. S., and A. F. Attia (2005), Forecasting the global temperature trend according to the predicted solar activity during the next decades, Mem. Soc. Astron. Ital., 76, 1030-1033. – reference: Angelov, P. P., and D. P. Filev (2004), An approach to online identification of Takagi-Sugeno fuzzy models, IEEE Trans. Syst. Man Cybern. B, 34(1), 484-498. – reference: Meng, B., and J. F. Zhang (2006), Reachability conditions for switched linear singular systems, IEEE Trans. Auto. Control, 51(3), 482-488. – reference: Tong, H. (1996), Nonlinear Time Series: A Dynamical System Approach, Oxford Univ. Press, New York. – reference: Cichoki, A., and R. Chichester (1993), Neural Networks for Optimization and Signal Processing, John Wiley, Hoboken, N. J. – reference: Casdagli, M. (1992), Chaos and deterministic versus stochastic and nonlinear modeling, R. Stat. Soc. B, 54, 303-328. – reference: Fry, C. D., M. Dryer, W. Sun, T. R. Detman, Z. K. Smith, C. S. Deehr, C.-C. Wu, S.-I. Akasofu, and D. B. Berdichevsky (2004), Solar observation-based model for multi-day predictions of interplanetary shock and CME arrivals at earth, IEEE Trans. Plasma Sci., 32, 4. – reference: Joglekar, P. J., and R. A. Agarwala (1973), Variation of atmospheric radio noise level with sunspot number, Proc. IEEE, 61, 252-253. – reference: Taniguchi, T., K. Tanaka, and H. O. Wang (2000), Fuzzy descriptor systems and nonlinear model following control, IEEE Trans. Fuzzy Syst., 8(4), 442-452. – reference: Dai, L. (1989), Singular Control Systems, Springer, New York. – reference: Gholipour, A., C. Lucas, B. N. Araabi, M. Mirmomeni, and M. Shafiee (2006), Extracting the main patterns of natural time series for long-term neurofuzzy prediction, Neural Comput. Appl., 16, 383-393, doi:10.1007/s00521-006-0062-x. – reference: Tong, H., and K. Lim (1980), Threshold autoregressive limit cycles and cyclical data, J. R. Stat. Soc., Ser. B, 42, 245-292. – reference: Marra, S., and F. C. Morabito (2006b), A new technique for solar activity forecasting using recurrent elman networks, Int. J. Comput. Intell., 3(1), 8-13. – reference: Dikpati, M., G. de Toma, and P. A. Gilman (2006), Predicting the strength of solar cycle 24 using a flux-transport dynamo-based tool, Geophys. Res. Lett., 33, L05102, doi:10.1029/2005GL025221. – reference: Gholipour, A., C. Lucas, B. N. Araabi, and M. Shafiee (2005), Solar activity forecast: spectral analysis and neurofuzzy prediction, J. Atmos. Sol. Terr. Phys., 67, 595-603. – reference: Sharifie, J., C. Lucas, and B. N. Araabi (2006), Locally linear neurofuzzy modeling and prediction of geomagnetic disturbances based on solar wind conditions, Space Weather, 4, S06003, doi:10.1029/2005SW000209. – reference: Wang, Y., Z. Q. Sun, and F. C. Sun (2004), Robust fuzzy control of a class of nonlinear descriptor systems with time-varying delay, Int. J. Control Auto. Syst., 2(1), 76-82. – reference: Campbel, S. L. (1980), Singular Systems of Differential Equation, Pitman, London. – reference: Weigend, A., B. H. Berman, and D. Rumelhart (1990), Predicting the future: A connectionist approach, Int. J. Neural Syst., 1(3), 193-209. – reference: Haykin, S. (1994), Neural Networks: A Comprehensive Foundation, Macmillan, New York. – reference: Verghese, G. C., B. C. Levy, and T. Kailath (1981), A generalized state-space for singular systems, IEEE Trans. Auto. 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Intell. – volume: 16 start-page: 383 year: 2006 end-page: 393 article-title: Extracting the main patterns of natural time series for long‐term neurofuzzy prediction publication-title: Neural Comput. Appl. – start-page: 3 year: 1997 end-page: 72 – volume: 2 start-page: 76 issue: 1 year: 2004 end-page: 82 article-title: Robust fuzzy control of a class of nonlinear descriptor systems with time‐varying delay publication-title: Int. J. Control Auto. Syst. – year: 1980 – year: 2002 – year: 2006 – year: 2004 – volume: 2 start-page: 557 year: 2002 end-page: 562 article-title: The prediction of maximum amplitudes of solar cycles and the maximum amplitude of solar cycle 24 publication-title: Chin. J. Astron. Astrophys. – year: 1997 – volume: 19 start-page: 403 year: 2000 end-page: 415 article-title: The role of the sun in climate forcing publication-title: Quat. Sci. Rev. – volume: 7 start-page: 27 issue: 1 year: 1985 end-page: 33 article-title: Wolf J. 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| Title | Forecasting sunspot numbers with the aid of fuzzy descriptor models |
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