A reinforcement learning intelligent controller based on primary-secondary response mechanism of immune system

Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms

Volumn 14, Issue 4, 2007, Pages 557-573

  Liu, Bao ^a   Ding, Yongsheng ^a   Ruan, Da ^b  

^a DONGHUA UNIVERSITY   (China)

^b BELGIAN NUCLEAR RESEARCH CENTRE   (Belgium)

Author keywords

Evolution; Immune system; Intelligent controller; Memory; Primary secondary response mechanism; Reinforcement learning

Indexed keywords

EID: 34249985025     PISSN: 14928760     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. Y.-S. Ding, Computational Intelligence: Theory, Technique, and Application, Scientific Publishing House, Beijing, China, (2004).
2. T. Kato, Y. Ninomiya, and I. Masaki, Preceding vehicle recognition based on learning from sample images, IEEE Transactions on Intelligent Transportation Systems, 3, (2002) 252-260.
3. K. L. Zhou and D. W. Wang, Digital repetitive learning controller for three-phase CVCF PWM inverter, IEEE Transportation on Industrial Electronics, 48(4), (2001) 820-830.
4. G. Oriolo, S. Panzieri, and G. Ulivi, An iterative learning controller for nonholonomic mobile robots, International Journal of Robotics Research, 17(9), (1998) 954-970.
5. F. H. Dean, F. John, G. Maria, and S. James, Fast connectionist learning for trailer backing using a real robot, Proceedings of IEEE International Conference on Robotics and Automation, (1996) 1917-1922.
6. J. S. Alexander, L. C. Baird, W. L. Baker, and J. A. Farrell, A design and simulation tool for connectionist learning control systems: Application to autonomous underwater vehicles, Proceedings of the Society for Computer Simulation Conference, Baltimore, Maryland, July 22-24, (1991) 771-776.
7. C. Druet, D. Ernst, and L. Wehenkel, Application of reinforcement learning to electrical power system closed-loop emergency control, Proceedings of Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000, Lyon, France, September, 1910, (2000) 86-95.
8. P. S. Braga, A topological reinforcement learning agent for navigation, Neural Computation and Applications, 12, (2003) 220-236.
9. P. K. Leslie, L. L. Michael, and W. M. Andrew, Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, (1996) 237-285.
10. D. B. Gu, H. S. Hu, and L. Spacek, Learning fuzzy logic controller for reactive robot behaviours, Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Kobe, Japan, July 20-24, 1, (2003): 46-51.
11. C. Rerni, Feedforward neural networks in reinforcement learning applied to high-dimensional motor control. Proceedings of Algorithmic Learning Theory: 13th International Conference, ALT 2002, Lubeck, Germany, November 24-26, (2002) 403-413.
12. C. Anderson, D. Hittle, A. Katz, and R. Kretchmar, Synthesis of reinforcement learning, neural networks, and PI control applied to a simulated heating coil, Journal of Artificial Intelligence in Engineering, 11(4), (1997) 423-431.
13. S. Norihisa, A. Masaharu, and K. Makoto, Control of associative chaotic neural networks using reinforcement learning, Proceedings of Advances in Neural Networks - ISNN 2004: International Symposium on Neural Networks, Dalian, China, August 2004, 395-400.
14. Y. Cang, H. C. Y. Nelson, and D. W. Wang, A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance, IEEE Transactions on Systems, Man, and Cybernetics, 33(1), (2003) 17-27.
15. A. O. Esogbue, W. E. Hearnes, and Q. Song, A reinforcement learning fuzzy controller for set-point regulator problems, Proceedings of the IEEE 5th International Fuzzy Systems Conference, New Orleans, LA, September 8-11, (1996) 2136-2142.
16. D. Dasgupta, Artificial Immune Systems and Their Applications, Springer-Verlag. Inc. (1999).
17. N. D. C. Leandro and T. Jonathan, Artificial Immune System: A New Computational Intelligence Approach, Springer-Verlag. Inc. (2002).
18. M.-Y. Cai, Medical Immunology, Scientific Publishing House, Beijing, China, (2002).
19. Y.-S. Ding and L.-H. Ren, Fuzzy self-tuning immune feedback controller for tissue hyperthermia, Proceedings of The Ninth. IEEE International Conference, Fuzzy IEEE 2000, San Antonio, Texas, USA, May 7-10, 1, (2000) 534-538.
20. Y.-S. Ding and L.-H. Ren, A novel fuzzy self-tuning immune feedback control system, Control and Decision (in Chinese), 15(4), (2000) 443-446.
21. Z.-Q. Qi and S.-M. Song, A novel immune feedback control algorithm and its applications, Proceedings of Genetic and Evolutionary Computation Conference-GECCO 2004, Seattle, WA, USA, June 26-30, (2004) 318-320.
22. K. Motohiro, S. Minoru, and T. Kazuhiko, Adaptive learning method of network controller using an immune feedback, Proceedings of the 1999 IEEE/ASME International Conference Advanced Intelligent Mechatronics, Atlanta, USA, September 19-23, (1099) 641-646.
23. K. Takashi and T. Yamada, Application of an immune feedback mechanism to control system, JSME International Journal, Series C, 41(2), (1998) 184-191.
24. D. H. Kim, PID controller tuning of a boiler control system using immune algorithm typed neural network, Proceedings of Computational Science-ICCS 2004: 4th International Conference, Krakow, Poland, June 6-9, (2004) 695-698.
25. D. H. Kim and J. I. Park, Intelligent PID control by immune algorithms based fuzzy rule auto-tuning, Lecture Notes in Computer Science, 2715, (2003) 474-482.