Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger

Expert Systems with Applications

Volumn 32, Issue 3, 2007, Pages 911-918

  Rouhani, Hossein ^a   Jalili, Mahdi ^a   Araabi, Babak N ^a   Eppler, Wolfgang ^b   Lucas, Caro ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

^b KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

Emotion based learning; Heat exchanger; Intelligent control; Locally linear models; Neurofuzzy models; Nonlinear system identification

Indexed keywords

ALGORITHMS; FUZZY SETS; HEAT EXCHANGERS; INTELLIGENT CONTROL; MATHEMATICAL MODELS; NEURAL NETWORKS;

EMOTION BASED LEARNING; LOCALLY LINEAR MODELS; NEUROFUZZY MODELS; NONLINEAR SYSTEM IDENTIFICATION;

LEARNING SYSTEMS;

EID: 33751002356     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2006.01.047     Document Type: Article

References (21)

1. Balkenius C., and Moren J. Emotional learning: a computational model of the amygdale. Cybernetics and Systems 32 6 (2000) 611-636
2. Brandner, J. J., & Schubert, K. (2005). Fabrication and testing of microstructure heat exchangers for thermal applications. In Proceedings of the 3rd international conference on microchannels and minichannels.
3. Eppler, W., & Beck, H. N. (1999). Piecewise linear networks (PLN) for function approximation. In Proceedings of IJCNN'99, pp. 388-391.
4. Fatourechi, M., Lucas, C., & Khaki Sedigh, A. (2001a). Reducing control effort by means of emotional learning. In Proceedings of the 9th Iranian conference on electrical engineering (ICEE2001), May, Tehran, Iran, pp. 41-1-41-8.
5. Fatourechi, M., Lucas, C., & Khaki Sedigh, A. (2001b). Reduction of maximum overshoot by means of emotional learning. In Proceedings of the 6th Annual CSI Computer Conference, February, Isfahan, Iran, pp. 460-467.
6. Fatourechi M., Lucas C., and Khaki Sedigh A. Emotional learning as a new tool for development of agent based system. Informatica 27 2 (2003) 137-144
7. Fink A., Fischer M., Nelles O., and Isermann R. Supervision of nonlinear adaptive controllers based on fuzzy models. Control Engineering Practice 8 (2000) 1093-1105
8. Fink A., Topfer S., and Isermann R. Nonlinear model-based control with local linear neuro-fuzzy models. Archive of Applied Mechanics 72 (2003) 911-922
9. Hafner M., Schukler M., Nelles O., and Isermann R. Fast neural networks for diesel engine control design. Control Engineering Practice 8 (2001) 1211-1221
10. Henning T., Brandner J.J., and Schubert K. Characterization of electrically powered micro-heat exchangers. Chemical Engineering Journal 101 (2004) 339-345
11. Inoue, K., Kawabata, K., & Kobayashi, H. (1996). On a decision making system with emotion. In Proceedings of the 5th IEEE international workshop on robot and human communication, pp. 461-465.
12. Jang J.S.R. Adaptive-network-based fuzzy inference system. IEEE Transactions on System, Man and Cybernetics 23 3 (1993) 665-685
13. Lucas C., Abbaspour A., Gholipour A., Nadjar Araabi B., and Fatourechi M. Enhancing the performance of neurofuzzy predictors by emotional learning algorithm. Informatica 27 2 (2003) 165-174
14. Lucas C., Shahmirzadi D., and Sheikholeslami N. Introducing BELBIC: brain emotional learning based intelligent controller. International Journal of Intelligent Automation and Soft Computing 10 1 (2004)
15. Miyazaki, K., Araki, N., Mogi, E., Kobayashi, T., Shigematsu, Y., & Ichikawa, M., et al. (1998). Brain learning control representation in nucleus accumbens. In Proceedings of the second international conference on knowledge-based intelligent electronic systems, Australia, April, pp. 21-23.
16. Moren, J. (2002). Emotion and learning: a computational model of the amygdala, PhD Thesis, Lund university, Lund, Sweden.
17. Neese R. Emotional disorders in evolutionary perspective. British Journal of Medical Psycology 71 (1998) 397-415
18. Nelles, O. (1997). Orthonormal basis functions for nonlinear system identification with local linear model trees (LoLiMoT). In Proceedings of IFAC symposium on system identification, Kitakyushu, Fukuoka, Japan.
19. Nelles O. Nonlinear system identification: From classical approaches to neural networks and fuzzy models (2001), Springer
20. Savinov, A. A. (1999). An algorithm for induction of possibilities set-valued rules by finding prime disjunctions. In Proceedings of the fourth on-line world conference on soft computing in industrial applications (WSC4), September 21-30.
21. Sugeno M., and Kang G.T. Structure identification of fuzzy model. Fuzzy Sets and Systems 28 (1988) 15-33