Enhancing the performance of Neurofuzzy predictors by emotional learning algorithm

Informatica (Ljubljana)

Volumn 27, Issue 2, 2003, Pages 137-145

  Lucas, Caro ^a,b   Abbaspour, Ali ^a   Gholipour, Ali ^a   Araabi, Babak N ^a   Fatourechi, Mehrdad ^c  

^a UNIVERSITY OF TEHRAN   (Iran)

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

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Emotional Learning; Neurofuzzy Model; Nonlinear Time Series; Prediction

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; FUZZY SETS; LEARNING SYSTEMS;

NEUROFUZZY PREDICTORS;

NEURAL NETWORKS;

EID: 0042673999     PISSN: 03505596     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (35)

1. Brown M., Harris C.J. (1994), Neuro fuzzy adaptive modeling and control, Prentice Hall.
2. Nelles O. (2001), Nonlinear system identification, Springer Verlag, Berlin.
3. Bossley K.M. (1997) Neurofuzzy Modeling Approaches in System Identification, PhD thesis, University of Southampton, Southampton, UK
4. Leung H., Lo T., Wang S. (2001) Prediction of noisy chaotic time series using an optimal radial basis function neural network, IEEE Tran. On Neural Networks, 12(5), pp. 1163-1172.
5. Takagi T., Sugeno M. (1985), Fuzzy identification of systems and its applications to modeling and control, IEEE Tran. On systems, Man and Cybernetics, vol. 15, pp. 116-132.
6. Kavli T. (1993) ASMOD: An algorithm for adaptive spline modeling of observation data, Int. J. of Control, 58(4), pp. 947-967.
7. Jang J.R. (1993) ANFIS: Adaptive network based fuzzy inference system, IEEE Tran. On systems, Man and Cybernetics, 23(3), pp. 665-685.
8. Goleman D. (1995) Emotional Intelligence, New York: Bantam Books.
9. Picard R.W., Vyzas E., Healey J. (2001) Toward machine emotional intelligence: Analysis of affective physiological state, IEEE Trans. on Pattern Analysis and Machine Intelligence, 23 (10), pp. 1175-1191.
10. Lucas C., Shahmirzadi D., Sheikholeslami N. (2003) Introducing BELBIC: Brain Emotional Learning Based Intelligent Controller, Accepted for publication in the International Journal of Intelligent Automation and Soft Computing (Autosoft).
11. th Iranian Conference on Electrical Engineering, ICEE'99, Tehran, Iran, pp. 207-212.
12. Lucas C., Jazbi S.A., Fatourechi M., Farshad M. (2000) Cognitive Action Selection with Neurocontrollers, Third Irano-Armenian Workshop on Neural Networks, Yerevan, Armenia.
13. Fatourechi M., Lucas C., Khaki Sedigh A. (2001) An Agent-based Approach to Multivariable Control, Proc. of IASTED International Conference on Artificial Intelligence and Applications, Marbella, Spain, pp. 376-381.
14. Fatourechi M., Lucas C., Khaki Sedigh A. (2001) Reducing Control Effort by means of Emotional Learning, Proc. of 9th Iranian Conf. on Electrical Engineering ICEE'01, Tehran, Iran, pp. 41-1 to 41-8.
15. Fatourechi M., Lucas C., Khaki Sedigh A. (2001) Reduction of Maximum Overshoot by means of Emotional Learning, Proceedings of 6th Annual CSI Computer Conference, Isfahan, Iran, pp. 460-467.
16. Perlovsky L.I. (1999), Emotions, Learning and control, proc. of IEEE Int. symp. On Intelligent control/Intelligent systems and semiotics, Cambridge MA, pp. 132-137.
17. Ventura R., Pinto Ferreira C. (1999) Emotion based control systems, proc. of IEEE Int. symp. On Intelligent control/Intelligent systems and semiotics, Cambridge MA, pp. 64-66.
18. th IEEE International Workshop on Robot and Human Communication, pp. 461-465.
19. Sutton R.S., Barto A.G. (1998) Introduction to reinforcement learning, MIT press, Cambridge.
20. Barto A., Sutton R., Watkins C. (1990) Learning and sequential decision making, in Learning and Computational Neuroscience, MIT press, Cambridge.
21. Watkins C. (1989) Learning from delayed rewards, PhD thesis, University of Cambridge, UK.
22. Watkins C., Dayan P. (1992) Q-Learning, Machine Learning, 8, pp. 279-292.
23. Sutton R.S. (1989) Learning to predict by the method of temporal differences, Machine Learning, 3, pp. 9-44.
24. Ungar L.H. (2002) Reinforcement learning from limited observations, Workshop on Learning and Approximate Dynamic Programming, Playacar, Mexico.
25. Brown M., Bossley K.M., Mills D.J., Harris C.J. (1995) High dimensional neurofuzzy systems:. Overcoming the curse of dimensionality, proc. of IEEE International Conference on Fuzzy Systems, New Orleans, USA, pp. 2139-2146.
26. Izeman A. J. (1985) Wolf J.R. and the Zurich sunspot relative numbers, The Mathematical Intelligence, vol.7, no.l, pp. 27-33.
27. Tong H., Lim K. (1980) Threshold Autoregressive limit cycles and cyclical data, J. Roy. Statistics. Soc. B, no.42, pp. 245-292.
28. Tong H. (1996) Nonlinear time series: A dynamical system approach, Oxford press, UK.
29. Weigend A., Huberman B., Rumelhart D.E. (1990) Predicting the future: a connectionist approach, Int. J. Of Neural systems, vol. 1, pp. 193-209.
30. Weigend A., Huberman B., Rumelhart D.E., (1992) Predicting sunspots and exchange rates with connectionist networks, in Nonlinear Modeling and Forecasting, Casdagli, Eubank: Editors, Addison- Wesley, pp. 395-432.
31. Werbos P.J. (1974) Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, PhD thesis, Harvard University, USA.
32. Rumelhart D.E., Hinton G.E., Williams R.J. (1986) Learning internal representations by error propagation, in D.E. Rumelhart and J.L. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, chapter 8, MIT Press, Cambridge.
33. Narendra K.S., Parthasarathy K. (1990) Identification and Control of dynamical systems using neural networks, IEEE Tran. On Neural Networks, 1(1), pp. 4-27.
34. Xu X., He H.G., Hu D. (2002) Efficient Reinforcement Learning Using Recursive Least-Squares Methods, Journal of Artificial Intelligence Research, 16, pp. 259-292.
35. Bay J.S. (1997) Behavior Learning in Large Homogeneous Populations of Robots, IASTED International Conference on Artificial Intelligence and Soft Computing, pp. 137-140.