A fuzzy Actor-Critic reinforcement learning network

Information Sciences

Volumn 177, Issue 18, 2007, Pages 3764-3781

  Wang, Xue Song ^a   Cheng, Yu Hu ^a   Yi, Jian Qiang ^b  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b INSTITUTE OF AUTOMATION   (China)

Author keywords

Actor Critic learning; Fuzzy inference system; Radial basis function neural network; Reinforcement learning

Indexed keywords

COMPUTATIONAL METHODS; DATA STORAGE EQUIPMENT; FUNCTION EVALUATION; FUZZY INFERENCE; NETWORK ARCHITECTURE; RADIAL BASIS FUNCTION NETWORKS;

FUZZY ACTOR CRITIC REINFORCEMENT LEARNING NETWORK (FACRLN); FUZZY INFERENCE SYSTEMS;

REINFORCEMENT LEARNING;

EID: 34250731840     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2007.03.012     Document Type: Article

References (30)

1. Anderson C.W. Learning to control an inverted pendulum using neural networks. IEEE Control System Magazine 9 3 (1989) 31-37
2. Barto A.G., Sutton R.S., and Anderson C.W. Neuron like elements that can solve difficult learning control problems. IEEE Transactions on System, Man and Cybernetics 13 5 (1983) 834-846
3. Berenji H.R. A reinforcement learning-based architecture for fuzzy logic control. International Journal of Approximate Reasoning 6 2 (1992) 267-292
4. Berenji H.R., and Khedkar P. Learning and tuning fuzzy logic controllers through reinforcements. IEEE Transactions on Neural Networks 3 5 (1992) 724-740
5. Box G.E.P., Jenkins G.M., and Reinsel G.C. Time series analysis, forecasting and control (1994), Prentice-Hall Publisher, Inc.
6. Canelon J.I., Shieh L.S., and Karayiannis N.B. A new approach for neural control of nonlinear discrete dynamic systems. Information Sciences 174 3-4 (2005) 177-196
7. Hwang K.S., Tan S.W., and Tsai M.C. Reinforcement learning to adaptive control of nonlinear systems. IEEE Transactions on Systems, Man and Cybernetics 33 3 (2003) 514-521
8. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23 3 (1993) 665-685
9. Jang J.S.R., and Sun C.T. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks 4 1 (1993) 156-159
10. Jin Y.C., and Sendhoff B. Extracting interpretable fuzzy rules from RBF Networks. Neural Processing Letters 17 2 (2003) 149-164
11. Jouffe L. Fuzzy inference system learning by reinforcement learning. IEEE Transactions on Systems, Man and Cybernetics 28 3 (1998) 338-355
12. Kazemian H.B., and Li M. A fuzzy control scheme for video transmission in Bluetooth wireless. Information Sciences 176 9 (2006) 1266-1289
13. Kondo T., and Ito K. A reinforcement learning with evolutionary state recruitment strategy for autonomous mobile robots control. Robotics and Autonomous Systems 46 2 (2004) 111-124
14. Lee C.C. Fuzzy logic in control systems: fuzzy logic controller - Part I and II. IEEE Transactions Systems, Man and Cybernetics 20 2 (1990) 404-435
15. Lin C.T., and Lee C.S.G. Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems. IEEE Transactions on Fuzzy Systems 2 1 (1994) 41-63
16. Lin C.J., and Lin C.T. Reinforcement learning for an ART-based fuzzy adaptive learning control network. IEEE Transactions on Neural Networks 7 3 (1996) 709-731
17. Lin C.T., Lin C.J., and Lee C.S.G. Fuzzy adaptive learning control network with on-line neural learning. Fuzzy Sets and Systems 71 1 (1995) 25-45
18. Mbede J.B., Ele P., Abia C.M.M., Toure Y., Graefe V., and Ma S.G. Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems. Information Sciences 171 4 (2005) 447-474
19. Meghabghab G., and Kandel A. Stochastic simulations of web search engines: RBF versus second-order regression models. Information Sciences 159 1-2 (2004) 1-28
20. Melin P., and Castillo O. Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system. Information Sciences 170 2-4 (2005) 133-151
21. Moody J., and Darken C.J. Fast learning in networks of locally-tuned processing units. Neural Computation 1 2 (1989) 281-294
22. Preux P., Delepoulle S., and Darcheville J.C. A generic architecture for adaptive agents based on reinforcement learning. Information Sciences 161 1-2 (2004) 37-55
23. Samejima K., and Omori T. Adaptive internal state space construction method for reinforcement learning of a real-world agent. Neural Networks 12 7-8 (1999) 1143-1155
24. Ster B., and Dobnikar A. Adaptive radial basis function decomposition by learning vector quantization. Neural Processing Letters 18 1 (2003) 17-27
25. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 15 1 (1985) 116-132
26. Ting C.S. Stability analysis and design of Takagi-Sugeno fuzzy systems. Information Sciences 176 19 (2006) 2817-2845
27. Wang L.X., and Mendel J.M. Fuzzy basis functions, universal approximation, and orthogonal least squares. IEEE Transactions on Neural Networks 3 5 (1992) 807-814
28. Wang W.J. New similarity measures on fuzzy sets and on elements. Fuzzy Sets and Systems 85 3 (1997) 305-309
29. Yi J.Q., Yubazaki N., and Hirota K. Anti-swing and positioning control of overhead traveling crane. Information Sciences 155 1-2 (2003) 19-42
30. Zhou C.J. Robot learning with GA-based fuzzy reinforcement learning agents. Information Sciences 145 1-2 (2002) 45-68