Abstract
This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm is proposed for constructing the NN-FLCS dynamically. The proposed dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNNFLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCs) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. Associated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal.
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References
Barto, A. G., Sutton, R. S. and Anderson, C. W. (1983) “Neuronlike adaptive elements that can solve difficult learning control problems,” IEEE Trans. Syst. Man Cybern., Vol. 13, No. 5, 834–847.
Franklin, J. A. (1989) “Input space representation for reinforcement learning control,” Proc. of 1989 IEEE Int’l Conf. Intelligent Machine, 115-122.
Hinton, G. E., McClelland, J. L. and Rumelhart, D. E. (1986) “Distributed representations,” in Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, 77–109.
Kosko, B. (1990) “Unsupervised learning in noise,” IEEE Trans, on Neural Networks, Vol. 1, No. 1, 44–57.
Lee, C. C. (1990) “Fuzzy logic in control systems: fuzzy logic controller – part I & II,” IEEE Trans. Syst. Man Cybern., Vol. SMC-20, No. 2, 404–435.
Lin, C. T. and Lee, C. S. G. Lee (1991) “Neural-network-based fuzzy logic control and decision system,” IEEE Trans, on Computers, Vol. C-40, No. 12, 1320–1336.
Lin, C. T. and Lee, C. S. G. Lee (1992) “Real-Time Supervised Structure/parameter Learning for Fuzzy Neural Network,” Proc. of 1992 IEEE Int’l Conf. on Fuzzy Systems, San Diego, CA, 1283–1290.
Lin, C. T. and Lee, C. S. G. Lee (1994) “Reinforcement Structure/Parameter Learning for an Integrated Fuzzy Neural Network,” IEEE Transactions on Fuzzy Systems, Vol. 2, No. 1, 46–63.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) “Learning internal representations by error propagation,” in Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, 318–362.
Sugeno, M. Ed. (1985) Industrial Applications of Fuzzy Control, Amsterdam: North Holland.
Sugeno, M. and Nishida, M. (1985) “Fuzzy control of model car,” Fuzzy sets Syst., Vol. 16, 103–113.
Sutton, R. S. (1988) “Learning to predict by the methods of temporal difference,” Machine Learning, Vol. 3, 9–44.
Tanscheit, R. and Scharf, E.M. (1988) “Experiments with the use of a rule-based self-organizing controller for robotics applications,” Fuzzy Sets Syst., Vol. 26, 195–214.
Werbos, P. J. (1990) “A menu of design for reinforcement learning over time,” in Neural Networks for Control, W.T. Miller, R.S. Sutton, and P.J. Werbos, eds, Cambridge: MIT Press.
Zadeh, L. A. (1988) “Fuzzy logic,” IEEE Computer, 83–93.
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© 1995 Kluwer Academic Publishers
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Lin, C.T., Lee, C.S.G. (1995). On the Structure and Learning of Neural-Network-Based Fuzzy Logic Control Systems. In: Bien, Z., Min, K.C. (eds) Fuzzy Logic and its Applications to Engineering, Information Sciences, and Intelligent Systems. Theory and Decision Library, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0125-4_7
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DOI: https://doi.org/10.1007/978-94-009-0125-4_7
Publisher Name: Springer, Dordrecht
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