Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning

Bounded 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, de...

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Bibliographic Details
Published inInternational journal of modelling & simulation Vol. 24; no. 2; pp. 65 - 72
Main Authors Gholipour, A., Lucas, C., Shahmirzadi, D.
Format Journal Article
LanguageEnglish
Published Anaheim, CA Taylor & Francis 01.01.2004
Calgary, AB Acta Press
Zürich
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
emotional learning
Exact sciences and technology
Multiobjective learning
prediction
Problem solving, game playing
reinforcement learning
Simulation
Software
space weather forecasting
Solar activity
Weather forecast
Simulation
Reinforcement learning
Multiobjective programming
Prediction
Geomagnetic activity
Algorithm
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Summary:Bounded 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 application of the proposed model indicates the significance of structural brain modelling beyond traditional neural networks, as well as the importance of biological motivation in choosing a suitable model for any given application.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0228-6203
1925-7082
DOI:10.1080/02286203.2004.11442288