Purposeful prediction of space weather phenomena by simulated emotional learning

International Journal of Modelling and Simulation

Volumn 24, Issue 2, 2004, Pages 65-72

  Gholipour, Ali ^a   Lucas, Caro ^a,b   Shahmirzadi, Danial ^a,b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b INSTITUTE FOR STUDIES IN THEORETICAL PHYSICS AND MATHEMATICS IPM   (Iran)

Author keywords

Emotional learning; Multiobjective learning; Prediction; Reinforcement learning; Space weather forecasting

Indexed keywords

ALARM SYSTEMS; BRAIN; DATA PROCESSING; DECISION MAKING; EVOLUTIONARY ALGORITHMS; GEOMAGNETISM; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATRIX ALGEBRA; NEURAL NETWORKS;

EMOTIONAL LEARNING; MULTIOBJECTIVE LEARNING; PREDICTION; REINFORCEMENT LEARNING; SPACE WEATHER FORECASTING;

WEATHER FORECASTING;

EID: 1642377611     PISSN: 02286203     EISSN: None     Source Type: Journal    
DOI: 10.1080/02286203.2004.11442288     Document Type: Article

References (37)

1. S. Chen, Y. Wu, & B.L. Luk, Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks, IEEE Trans. on Neural Networks, 10(5), 1999, 1239-1243.
2. H. Leung, T. Lo, & S. Wang, Prediction of noisy chaotic time series using an optimal radial basis function neural network, IEEE Trans. on Neural Networks, 12(5), 2001, 1163-1172.
3. H. Lundstedt, Neural networks and predictions of solar-terrestrial effects, Planet. Space Sci., 40, 1992, 457-464.
4. R.S. Sutton, Learning to predict by the method of temporal differences, Machine Learning, 3, 1989, 9-44.
5. R.S. Sutton & A.G. Barto, Introduction to reinforcement learning (Cambridge, MA: MIT Press, 1998).
6. A. Barto, R. Sutton, & C. Watkins, Learning and sequential decision making, in M. Gabriel and J.W. Moore (Ed.), Learning and Computational Neuroscience (Cambridge, MA: MIT Press, 1990), 539-602.
7. th IEEE Int. Workshop on Robot and Human Communication, 1996, Tsukuba, Japan, 461-465.
8. J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. on Systems, Man and Cybernetics, 23(3), 1993, 665-685.
9. A.J. Izeman, Wolf J.R. and the Zurich sunspot relative numbers, The Mathematical Intelligence, 7(1), 1985, 27-33.
10. R.J. Thompson, A technique for predicting the amplitude of solar cycle, Solar Physics, 148, 1993, 383.
11. K.H. Schatten & W.D. Pesnell, An early solar dynamo prediction: Cycle 23 - Cycle 22, Geophysical Research Letters, 20, 1993, 2257-2278.
12. C. Watkins, Learning from delayed rewards, doctoral diss., Cambridge, University, England, 1989.
13. C. Watkins, & P. Dayan, Q-Learning, Machine Learning, 8, 1992, 279-292.
14. M. Fatourechi, C. Lucas, & A. Khaki Sedigh, An agent-based approach to multivariable control, Proc. IASTED Int. Conf. on Artificial Intelligence and Applications, Marbella, Spain, 2001, 376-381.
15. M. Eatourechi, C. Lucas, & A. Khaki Sedigh, Reducing control effort by means of emotional learning, Proc. 9th Iranian Conf. on Electrical Engineering, ICEE'01 (4-1), Tehran, Iran, 2001, (41), 1-8.
16. L.I. Perlovsky, Emotions, learning and control, Proc. IEEE Int. Symp. on Intelligent Control/Intelligent Systems and Semiotics, Cambridge, MA, 1999, 132-137.
17. R. Ventura, & C. Pinto Ferreira, Emotion based control systems, Proc. IEEE Int. Symp. on Intelligent Control/Intelligent Systems and Semiotics, Cambridge, MA, 1999, 64-66.
18. J. Moren, Emotion and learning: A computational model of the amygdala, doctoral diss., Lund University, Lund, Sweden, 2002.
19. J. Moren & C. Balkenius, A computational model of emotional learning in the amygdala, in J.A. Mayer, A. Berthoz, D. Floreano, H.L. Roitblat, & S.W. Wilson (eds.), From animals to animals 6 (Cambridge, MA: MIT Press, 2000).
20. C. Lucas, D. Shahmirzadi, & X. Sheikholeslami, Introducing BELBIC: Brain emotional learning based intelligent controller, forthcoming in International Journal of Intelligent Automation and Soft Computing (Autosoft).
21. R.A. Rescorla & A.R. Wagner, A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement, in A.H. Black & W.F. Prokasy (eds.), Classical conditioning II: Current research and theory (New York: Appleton-Century-Crofts, 1972).
22. H. Tong & K. Lim, Threshold autoregressive limit cycles and cyclical data, J. Roy. Statistics. Soc. B, 42, 1980, 245-292.
23. A. Weigend, B. Huberman, & D.E. Rumelhart, Predicting the future: A connectionist approach, International Journal of Neural Systems, 1(3), 1990, 193-209.
24. O. Uluyol, M. Ragheb, & S.R. Ray, Local output gamma feedback neural network, Proc. IEEE Int. Conf. on Neural Networks: IJCNN, 1, 1998, 337-342.
25. F. Boberg, P. Wintoft, & H. Lundstedt, Real time Kp predictions from solar wind data using neural networks, Phys. Chem. Earth, 25(4), 2000, 275-280.
26. L.F. Bargatze, D.N. Baker, R.L. McPherron, & E.W. Hones, Jr., Magnetospheric impulse response for many levels of geomagnetic activity, Journal of Geophysical Research, 90, 1985, 6387-6394.
27. D. Vassiliadis, A.J. Klimas, D.N. Baker, & D.A. Roberts, A description of the solar wind magnetosphere coupling based on nonlinear filters, Journal of Geophysical Research, 100, 1995, 3495-3512.
28. H. Lundstedt & P. Wintoft, Prediction of geomagnetic storms from solar wind data using a neural network, Ann. Geophysicae, 12, 1994, 19-24.
29. H. Gleisner, H. Lundstedt, & P. Wintoft, Predicting geomagnetic storms from solar wind data using time delay neural networks, Ann. Geophysicae, 14, 1996, 679-686.
30. H. Gleisner, Solar wind and geomagnetic activity: Predictions using neural networks, doctoral diss., Lund University, Lund, Sweden, 2000.
31. D. Goleman, Emotional intelligence (New York: Bantam Books, 1995).
32. C. Lucas, A. Abbaspour, A. Gholipour, B. N. Araabi, & M. Fatourechi, Multi objective emotional learning based fuzzy inference system, WSEAS Transactions on Systems, 4(2), October 2003, 1094-1101.
33. C. Lucas, A. Abbaspour, A. Gholipour, B. N. Araabi, & M. Fatourechi, Enhancing the performance of neurofuzzy predictors by emotional learning algorithm, Informatica, 27(2), 2003, 165-174.
34. th Iranian Conf. on Electrical Engineering, ICEE'99, Tehran, Iran, 1999, 207-212.
35. R.W. Picard, E. Vyzas, & J. Healey, Toward machine emotional intelligence: Analysis of affective physiological state, IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(10), 2001, 1175-1191.
36. L.H. Ungar, Reinforcement learning from limited observations, Workshop on Learning and Approximate Dynamic Programming, Playacar, Mexico, 2002.
37. S. Marshall, M. St. John, & D. Tucker, Combining EEG, Pupil dilation techniques and performance metrics to measure cognition and attention in human-computer interaction, DARPA IPTO Bio-Bionics: Augmenting Cognition Workshop, Waikoloa, HI, 2003.