An Improved Model of Brain Emotional Learning Algorithm Based on Interval Knowledge

Volume 14, Issue 1, pp 42-53 http://dx.doi.org/10.22436/jmcs.014.01.05

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Authors

Yousef Sharafi - Computer Department of Islamic Azad University Science and Research Branch, Tehran, Iran Saeed Setayeshi - Faculty of Nuclear Engineering and Physics, Amirkabir University, Tehran, Iran Alireza Falahiazar - Computer Department of Islamic Azad University Science and Research Branch, Tehran, Iran

Abstract

The brain emotional learning algorithm inspired by a reduced system of a computational model simulates the brain learning performance quite simply with mimicking mammalian brains. The present paper endeavors to come forward to an improved model of the emotional learning algorithm based on the interval knowledge. In this proposed model, based on the interval knowledge, the weights of the amygdala and orbitofrontal sections will be updated. Eventually, the results of implementing and performing the improved brain emotional learning algorithm will be presented to be compared with the original version of the algorithm to Prediction the chaotic time series, Lorenz and Rossler, about which a noticeable improvement in its precision, accuracy and speed of convergence of the final results is reported.

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Keywords

• Brain Emotional Learning Algorithm
• Interval Knowledge
• Chaotic Time Series
• Prediction

MSC

•  68T05
•  91E40
•  92C20
•  92B20

References

• [1] A. Ghezelbash, Predicting Changes in Stock Index and Gold Prices to Neural Network Approach, The Journal of Mathematics and Computer Science, 4 (2012), 227-236.
  • Google Scholar


• [2] M. Bagheri, M. Valipour, V. Amin, The Bankruptcy Prediction in Tehran shareholding using Neural Network and it ’s Comparison with Logistic Regression, The Journal of Mathematics and Computer Science, 5 (2012), 219-228.
  • Google Scholar


• [3] M. M. Javidi, M. H. Nattaj, A New and Quick Method to Detect DoS Attacks by Neural Networks, The Journal of mathematics and computer Science, 6 (2013), 85-96.
  • Google Scholar


• [4] Z. Falahiazar, M. Rohani, A. Falahiazar, Controlling the False Alarm in an Intrusion Tolerant Database System Using Significance Degrees of Data Objects, Journal of mathematics and computer science, 13 (2014), 212-225.
  • Google Scholar


• [5] Martin T. Hagan, Mohammad B. Menhaj, Training feed forward networks with the Marquardt algorithm, Neural Networks, IEEE Transactions on 5, 6 (1994), 989-993.
  • View Article
  • Google Scholar


• [6] L. Falahiazar, M. Teshnehlab, A. Falahiazar, Parallel Genetic Algorithm Based on a New, in International Conference on Recent Advances in Computing and Software Systems, (2012)


• [7] Y. Sharafi, M. Ahmadieh Khanesar Teshnehlab, Discrete Binary Cat Swarm Optimization Algorithm, in Computer, Control & Communication (IC4), 3rd International Conference on, (2013)
  • View Article
  • Google Scholar


• [8] J. M. Jenkins, K. Oatley, N. L. Stein, Human emotions: a reader, Blockwell, (1998)
  • Google Scholar


• [9] E. T. Rolls, Neurophysiology and Functions of the Primate Amygdala. In: The Amygdala: Neurobiologycal Aspects of Emotion, Memory and Mental Dysfunction, (1992)
  • Google Scholar


• [10] J. Le Doux, The Emotional Brain , Simon and Schuster, New York (1996)
  • Google Scholar


• [11] J. L. McGaugh, F. Bermudez-Rattoni, R. A. Prado-Alcala, Plasticity in the Central Nervous System: Learning and Memory, Lawrence Erlbaum Associates, Inc., Mahwah, NJ., (1995), 17-39.
  • Google Scholar


• [12] 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. , (2000), 115-124.
  • Google Scholar


• [13] P. Lingras, Rough Neural Networks, in: Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty, Granada, (1996), 1445–1450.
  • Google Scholar


• [14] S. Chandanal, V. Mayorga, The New Rough Neuron, Neural Networks and Brain, Beijing, (2005), 13-18.
  • View Article
  • Google Scholar


• [15] C. Balkenius, J. Morén, Emotional Learning: A Computational Model of Amygdala, Cybernetics and Systems, 32 (2001), 611-636.
  • View Article
  • Google Scholar
  • MATH


• [16] C. Lucas, D. Shahmirzadi, N. Sheikholeslami, Introducing BELBIC: Brain emotional learning based intelligent controller, Int. J. Intell. Autom. Soft Comput. , 10 (2004), 11-21.
  • View Article
  • Google Scholar


• [17] E. Lotfi, M. R. Akbarzadeh, Supervised Brain Emotional Learning, Int. J. Intell. Autom. WCCI 2012 IEEE World Congress on Computational Intelligence, June, Brisbane, Australia, (2012), 10-15.
  • View Article
  • Google Scholar