Hybrid intelligent systems applied to the pursuit-evasion game

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2009, Pages 2603-2608

  Desouky, Sameh F ^a   Schwartz, Howard M ^a  

^a CARLETON UNIVERSITY   (Canada)

Author keywords

ANFIS; Fuzzy logic controller; Genetic algorithms; Hybrid intelligent system; Pursuit evasion game

Indexed keywords

ADAPTIVE NETWORK FUZZY INFERENCE SYSTEMS; ADAPTIVE NETWORKS; ANFIS; FUZZY LOGIC CONTROLLERS; HYBRID INTELLIGENT SYSTEM; ITERATIVE PROCESS; OPTIMAL STRATEGIES; PD CONTROLLERS; PURSUIT EVASION GAMES; TWO STAGE;

COMPUTER SIMULATION; CONTROLLERS; CYBERNETICS; FUZZY INFERENCE; FUZZY SYSTEMS; GENETIC ALGORITHMS; INTELLIGENT SYSTEMS; OPTIMAL SYSTEMS; OPTIMIZATION;

ADAPTIVE CONTROL SYSTEMS;

EID: 74849091001     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2009.5346113     Document Type: Conference Paper

References (30)

1. T. L. Seng, M. B. Khalid, and R. Yusof, "Tuning of a neuro-fuzzy controller by genetic algorithm," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernatics, vol. 29, pp. 226-236, Apr. 1999.
2. S. Labiod and T. M. Guerra, "Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems," Fuzzy Sets and Systems, vol. 158, no. 10, pp. 1126-1137, 2007.
3. H. K. Lam and F. H. F. Leung, "Fuzzy controller with stability and performance rules for nonlinear systems," Fuzzy Sets and Systems, vol. 158, pp. 147-163, 2007.
4. M. Mucientes, D. L. Moreno, A. Bugarin, and S. Barro, "Design of a fuzzy controller in mobile robotics using genetic algorithms," Applied Soft Computing, vol. 7, no. 2, pp. 540-546, 2007.
5. F. Hoffmann, "Evolutionary algorithms for fuzzy control system design," in Proceedings of the IEEE, vol. 89, pp. 1318-1333, Sep. 2001.
6. C. J. Lin and Y. J. Xu, "Efficient reinforcement learning through dynamic symbiotic evolution for TSK-type fuzzy controller design," International Journal of General Systems, vol. 34, pp. 559-578, Oct. 2005.
7. L. X. Wang, "Generating fuzzy rules by learning from examples," IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, pp. 1414-1427, Nov./Dec. 1992.
8. F. Herrera, M. Lozano, and J. L. Verdegay, "Tuning fuzzy logic controllers by genetic algorithms," International Journal of Approximate Reasoning, vol. 12, pp. 299-315, 1995.
9. A. Lekova, L. Mikhailov, D. Boyadjiev, and A. Nabout, "Redundant fuzzy rules exclusion by genetic algorithms," Fuzzy Sets and Systems, vol. 100, pp. 235-243, 1998.
10. L. Ming, G. Zailin, and Y. Shuzi, "Mobile robot fuzzy control optimization using genetic algorithm," Artificial Intelligence in Engineering, vol. 10, no. 4, pp. 293-298, 1996.
11. S. F. Desouky and H. M. Schwartz, "Genetic based fuzzy logic controller for a wall-following mobile robot," in 2009 American Control Conference, to appear.
12. E. Entchev and L. Yang, "Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation," Journal of Power Sources, vol. 170, pp. 122-129, 2007.
13. X. J. Wu, X. J. Zhu, G. Y. Cao, and H. Y. Tu, "Nonlinear modeling of a SOFC stack based on ANFIS identification," Simulation Modelling Practice and Theory, vol. 16, pp. 399-409, Apr. 2008.
14. E. R. Nucci, R. G. Silva, V. R. Souza, R. L. C. Giordano, R. C. Giordano, and A. J. G. Cruz, "Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of bacillus megaterium cellular concentrations," Bioprocess and biosystems engineering, vol. 30, pp. 429-438, 2007.
15. F. Daldaban, N. Ustkoyuncu, and K. Guney, "Phase inductance estimation for switched reluctance motor using adaptive neuro-fuzzy inference system," Energy Conversion and Management, vol. 47, pp. 485-493, 2006.
16. I. T. K. Güney, "Computation of association probabilities for single target tracking with the use of adaptive neuro-fuzzy inference system," Turkish Journal of Electrical Engineering and Computer Sciences, vol. 13, no. 1, pp. 105-118, 2005.
17. J. Baltes and Y. J. Park, "Comparison of several machine learning techniques in pursuit-evasion games," in RoboCup-01: Robot Soccer World Cup V, (New York), Springer, 2002.
18. J. Ge, L. Tang, J. Reimann, and G. Vachtsevanos, "Hierarchical decomposition approach for pursuit-evasion differential game with multiple players," in 2006 IEEE Aerospace Conference, (Big Sky, MT), Mar. 2006.
19. S. M. LaValle, Planning Algorithms. Cambridge University Press, 2006.
20. S. H. Lim, T. Furukawa, G. Dissanayake, and H. F. D. Whyte, "A time-optimal control strategy for pursuit-evasion games problems," in International Conference on Robotics and Automation, (New Orleans, LA), Apr. 2004.
21. J. H. Holland, "Genetic algorithms and the optimal allocation of trials," SIAM Journal on Computing, vol. 2, no. 2, pp. 88-105, 1973.
22. D. E. Goldberg, Genetic Algorithms for Search, Optimization, and Machine Learning. Reading: Addison-Wesley, 1989.
23. C. Karr, "Genetic algorithms for fuzzy controllers," AI Expert, vol. 6, no. 2, pp. 26-33, 1991.
24. T. L. Seng, M. Khalid, and R. Yusof, "Tuning of a neuro-fuzzy controller by genetic algorithms with an application to a coupled-tank liquid-level control system," International Journal of Engineering Applications on Artificial Intelligence, vol. 11, pp. 517-529, Sep. 1998.
25. J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp. 665-685, May/Jun. 1993.
26. L. Zhao and F. Y. Wang, "The design of self-organizing fuzzy neural networks based on GA-ECPSO and MBP," in IEEE International Conference on Systems, Man, and Cybernetics, ISIC, (Montreal, QC, Canada), pp. 1618-1623, Oct. 2007.
27. L. Zhao and F. Y. Wang, "Design for self-organizing fuzzy neural networks using a novel hybrid learning algorithm," in IEEE Congress on Evolutionary Computation, CEC, (Singapore), pp. 2972-2979, Sep. 2007.
28. Y. He, Y. Zhang, and L. Xiang, "Study of application model on BP neural network optimized by fuzzy clustering," in MICAI, pp. 712-720, 2005.
29. M. Negnevitsky, "Multi-layer neural networks with improved learning algorithms," in Digital Imaging Computing: Techniques and Applications, DICTA, (Cairns, Australia), pp. 1-6, Dec. 2005.
30. C. H. Cai, D. Du, and Z. Y. Liu, "Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy inference system (ANFIS)," in 12th IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1068-1073, May 2003.