Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning

AbstractBounded rationality and satisfying models, rather than optimization techniques, have shown good performance in decision making. The emotional learning algorithm is an example. It is based on simulating human emotions via reinforcement agents. A new approach towards purposeful prediction problems, derived from a recently developed model of emotional learning in human brain, is introduced in this article. The proposed algorithm inherently emphasizes learning to predict future peaks, and performs remarkably accurate predictions among the important regions, features, or objectives. Space weather forecasting is an excellent example of using this methodology, and in fact was the motivation to introduce purposeful prediction via multiobjective learning algorithm in this research. Three examples of predicting solar activity, geomagnetic activity, and geomagnetic storms show the characteristics of the suggested algorithm and its usefulness to space weather warning and alert systems. The successful applicat...

[1]  Daniel N. Baker,et al.  A description of the solar wind-magnetosphere coupling based on nonlinear filters , 1995 .

[2]  R. Rescorla,et al.  A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement , 1972 .

[3]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  L. I. Perlovsky.  Emotions, learning and control , 1999, Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014).

[5]  Daniel N. Baker,et al.  Magnetospheric Impulse Response for Many Levels of Geomagnetic Activity , 1985 .

[6]  H. Tong,et al.  Threshold Autoregression, Limit Cycles and Cyclical Data , 1980 .

[7]  O. Uluyol,et al.  Local output gamma feedback neural network , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[8]  P. Wintoft,et al.  Prediction of geomagnetic storms from solar wind data with the use of a neural network , 1994 .

[9]  Henry Leung,et al.  Prediction of noisy chaotic time series using an optimal radial basis function neural network , 2001, IEEE Trans. Neural Networks.

[10]  H. Gleisner,et al.  Predicting geomagnetic storms from solar-wind data using time-delay neural networks , 1996 .

[11]  Henrik Lundstedt,et al.  Neural networks and predictions of solar-terrestrial effects , 1992 .

[12]  Kuniaki Kawabata,et al.  On a decision making system with emotion , 1996, Proceedings 5th IEEE International Workshop on Robot and Human Communication. RO-MAN'96 TSUKUBA.

[13]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[14]  R. Ventura,et al.  Emotion-based control systems , 1999, Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014).

[15]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[16]  Sheng Chen,et al.  Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks , 1999, IEEE Trans. Neural Networks.

[17]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[18]  Kenneth H. Schatten,et al.  An early solar dynamo prediction : cycle 23 ∼ cycle 22 , 1993 .

[19]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[20]  A. Izenman.  J. R. Wolf and the zürich sunspot relative numbers , 1985 .

[21]  J. Morén,et al.  A computational model of emotional learning in the amygdala. , 2000 .

[22]  R. Thompson,et al.  A technique for predicting the amplitude of the solar cycle , 1993 .