A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index

Year 2014, , 22 - 25, 01.04.2014

Ehsan Lotfi , A. Keshavarz

https://doi.org/10.18201/ijisae.85494

Cited By: 3

Abstract

In this paper, we propose fuzzy mathematical model of brain limbic system (LS) which is responsible for emotional stimuli. Here the proposed model is utilized to predict the chaotic activity of the earth’s magnetosphere. Numerical results show that the correlation of the results obtained from the proposed fuzzy model is higher than non-fuzzy models. Hence, the proposed model can be applied in real time chaotic time series prediction.

Keywords

Cognitive science, Amygdala, Computational model, BELBIC, Chaotic time series

References

• J. Morén, Emotion and Learning - A Computational Model of the Amygdala, Lund University Cognitive Studies, 2002.
• J. E. LeDoux, “Emotion circuits in the brain,” Annual Review of Neuroscience, Vol. 23, pp. 155-184, 2000.
• J. E. LeDoux, The Emotional Brain, Simon and Schuster, New York, 1996.
• E. T. Rolls, “Neurophysiology and functions of the primate amygdala,” In: The Amygdala: Neurobiologycal Aspects of Emotion, Memory and Mental Dysfunction, New York, Wiley-Liss, pp. 143-165. 1992.
• L. Cahill, R.J. haier, J. Fallon, “Amygdala activity at encoding correlated with long-term, free recall of emotional information,” Proceedings-National Academy of Science USA, Vol. 93, pp. 8015-8021, 1996.
• A Bechara, H Damasio, AR Damasio “Different contributions of the human amygdala and Ventromedial Prefrontal Cortex to Decision-Making,” Journal of Neuroscience, Vol. 19, pp. 5473–5481, 1999.
• C. Balkenius, J. Morén, “Emotional learning: a computational model of amygdala,” Cybernetics and Systems, Vol. 32, pp. 611-636, 2001.
• J. Morén, C. Balkenius, “A computational model of emotional learning in the amygdala,” In: From Animals to Animats 6: Proceedings of the 6th International Conference on the Simulation of Adaptive Behaviour, Meyer, J.A., A. Berthoz, D. Floreano, H.L. Roitblat and S.W. Wilson (Eds.). MIT Press, Cambridge, MA., USA., pp. 115-124, 2000.
• C. Lucas, D. Shahmirzadi and N. Sheikholeslami, “Introducing BELBIC: brain emotional learning based intelligent controller,” International Journal of Intelligence Automotive Soft Computing, Vol. 10, pp. 11-21, 2004.
• C. Lucas, “BELBIC and its industrial applications: towards embedded neuroemotional control codesign,” Integrated Systems, Design and Technology, Vol. 3, pp. 203-214, 2010.
• H. Rouhani, M. Jalili, B.N. Araabi, W. Eppler, C. Lucas, “Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger,” Expert System and Application, Vol. 32, pp. 911-918, 2007.
• M. Samadi, A. Afzali-Kusha, C. Lucas, “Power management by brain emotional learning algorithm,” 7th International Conference on ASIC, pp. 78 – 81, 2007.
• E. Daryabeigi, G.R.A. Markadeh, C. Lucas, “Emotional controller (BELBIC) for electric drives — A review,” 36th Annual Conference on IEEE Industrial Electronics Society, pp. 2901 – 2907, 2010.
• M. Chandra, Analytical Study of A Control Algorithm Based on Emotional Processing, M.S. Dissertation, Indian Institute of Technology Kanpur, 2005.
• C. Lucas, R.M. Milasi, B.N. Araabi, “Intelligent modeling and control of washing machine using Locally Linear Neuro-Fuzzy (LLNF),” Asian Journal of Control, Vol. 8, pp. 393-400, 2006.
• S. Jafarzadeh, R. Mirheidari, M.R.J. Motlagh, M. Barkhordari, “Designing PID and BELBIC controllers in path tracking troblem,” International Journal of Computers Communications & Control, Vol. 3, pp. 343-348, 2008.
• A. Sadeghieh, H. Sazgar, K. Goodarzi, C. Lucas, “Identification and real-time position control of a servo-hydraulic rotary actuator by means of a neurobiologically motivated algorithm,” ISA Transactions, Vol. 51, pp. 208-219, 2012.
• A. M. Khalilian, Abedi, A.D. Zadeh,”Position control of hybrid stepper motor using brain emotional controller,” Energy Procedia, Vol. 14, pp. 1998-2004, 2012.
• A. Gholipour, Lucas, C. A. R. O., & Shahmirzadi, D. A. N. I. A. L. (2004), “Predicting geomagnetic activity index by brain emotional learning,” WSEAS AIKED, 3.
• E. Lotfi and Akbarzadeh-T, M. R., (2012). “Supervised brain emotional learning,” IEEE Int. Joint Conf. on Neural Networks (IJCNN), pp. 1-6, doi: 10.1109/IJCNN.2012.6252391
• E. Lotfi and Akbarzadeh-T., M. R., (2013), “Adaptive Brain Emotional Decayed Learning for Online Prediction of Geomagnetic Activity Indices,” Neurocomputing, doi: 10.1016/j.neucom.2013.02.040
• E. Lotfi, M. R. Akbarzadeh-T., 2013. “Emotional Brain-Inspired Adaptive Fuzzy Decayed Learning for Online Prediction Problems,” In Proc. IEEE International conference on fuzzy systems (FUZZ-IEEE 2013), July 7-10 2013, Hyderabad, India.
• T. Babaie, Karimizandi, C. Lucas, “Learning based brain emotional intelligence as a new aspect for development of an alarm system,” Soft Comput., Vol. 12, pp: 857–873, 2008.
• E. Lotfi, M. R. Akbarzadeh-T., 2013. “Brain Emotional Learning Based Pattern Recognizer,” Cybernetics & Systems, doi: 10.1080/01969722.2013.789652
• http://www.tandfonline.com/eprint/J9zxz4ivkYNQgWg9Bhs8/full
• E. Lotfi, 2013. “Mathematical modeling of emotional brain for classification problems,” Proceedings of Institute of Applied Mathematics, Vol. 2, No. 1, 2013.
• M. T. Hagan, H.B. Demuth, M.H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996.