Abstract
Neural networks and fuzzy logic have emerged recently as powerful techniques for implementation of complex nonlinear control systems. A persistent bottleneck, however, remains the manner in which suitable control strategies can be derived in both approaches. Each method requires optimization of several parameters, which is usually not a trivial problem for many optimization techniques. This chapter will discuss important issues related to rule-based fuzzy and neural systems. These issues are knowledge representation and knowledge acquisition. Knowledge representation refers to the manner in which knowledge can be expressed in connectionist neural systems in order to preserve the advantages of fuzzy-based systems. Knowledge acquisition, on the other hand, is the manner in which the rules that represent fuzzy knowledge are obtained and stored taking advantage of neural learning techniques. The primary objective of using fuzzy-neural approaches is to develop systems that are capable of automatic acquisition of knowledge for given network representations. In that regard the chapter will also discuss the implementation of learning using genetic algorithms in such neural-network-based fuzzy systems. Typical applications for such systems include the control of unknown nonlinear plants and this will be demonstrated in this chapter via a case study involving aero-engine control.
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References
Sutton R. Miller W.T. and Werbos P.J., editors. Neural Networks for Control MIT Press Cambridge, 1990.
Wang L.X. and Mendel J.M. Fuzzy basis functions, universal approximation and orthogonal least squares. IEEE Trans. on Neural Networks, pages 807–814, 1992.
Kong S. G. and Kosko B. Adaptive fuzzy systems for backing up a truck-and-trailer. IEEE Trans. on Neural Networks, 3(2):211–223, 1992.
Brown M. and Harris C.J. Fuzzy logic, neural networks and b-spline for intelligent control. IMA J. Math. Control and Info. Theory, 8:239–265, 1992.
Nie J. and Linkens D. A. Learning control using fuzzified self-organising radial basis function network. IEEE Trans. on Fuzzy Syst., 1(4):280–287, 1993.
Buckley J. J. and Hayashi Y. Fuzzy iinput-output controllers are universal approxima-torshybrid neural nets can be fuzzy controllers and fuzzy expert systems. Fuzzy Sets and Systems, 58:273–278, 1993.
Lin C. T. and Lee C. S. G. Reinforcement structure/parameter learning for neural network based fuzzy logic control systems. IEEE Trans. on Fuzzy Syst., 2(1):46–63, 1994.
Berenji H.R. and Khedkar P. Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans. on Neural Networks, 3(5):724–740, 1992.
Esogbue A.O. and Murrell J.A. A fuzzy adaptive controller using reinforcement learning neural networks. In Proc. IEEE Int. Conf. Fuzzy Systems, pages 178–183, 1993.
Holland J.H., editor. Adaptation in Natural and Artificial Systems. Addison-Wesley Publishers, 1975.
D.E. Goldberg, editor. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishers, 1989.
Linkens D.A. and Nyongesa H.O. Genetic algorithms for fuzzy control. Proc. IEE. pt. D-Control theory and appl., 142(3):161–185, 1995.
Karr C.L. Design of an adaptive fuzzy logic using a genetic algorithm. In Proc. Proc. Int. Conf. on Genetic Algorithms, 1989.
Kosko B. Fuzzy systems as universal approximators. In Proc. IEEE Int. Conf. Fuzzy Syst., pages 1153–1162, 1992.
Lee C.C. Fuzzy logic in control systems. IEEE Trans. on Syst., Man & Cybernetics, 20:404–435, 1990.
Zadeh L.A. Fuzzy sets. Information & Control, 8:338–353, 1965.
Zadeh L.A. Fuzzy logic, neural networks and soft computing. Communications of the ACM, 37(3):77–84, 1994.
Hinton G.E. Rumelhart D.E. and Williams R.J., editors. Learning internal representations by error backpropagation, In Parallel distributed processing. MIT press, 1986.
Hecht-Nielsen R., editor. Neurocomputing. Addison Wesley, 1987.
Albus J. S. A new appraoch to manipulator control: the cerebellar model articulation controller (cmac). Journal of Dynamic Systems, Measurement and Control, 97:220–227, 1975.
Koza J.R., editor. Genetic Programming: on the programming of computers by means of natural selection. MIT Press, 1992.
Fogel D.B. On the philosophical differences between evolutionary algorithms and genetic algorithms. In Proc. Second Annual Conf. Evolutionary Programming, 1993.
Holland J.H. Genetic algorithms and the optimal allocation of trials. SIAM J. Comput., 2(2):89–104, 1973.
Davis L., editor. A Handbook of Genetic Algorithms. Van Nostrand Reinhold Publishers, 1990.
Park J. and Sandberg I.W. Universal approximation using radial basis function networks. Neural Computation, 3:245–257, 1991.
Whitley D. Schaffer J.D. and Eshelman L.J. Combinations of genetic algorithms and neural networks: a survey of the state of the art. In Proc. Int. Conf. Combination of GA and NN, pages 1–37, 1992.
Papaikonomou A. Petridis V., Kazarlis S. and A. Filelis. A hybrid genetic algorithm for training neural networks. In Proc. Int. Conf Artificial NN, pages 953–956, 1992.
Lyung S.L. and Adeli H. A parallel genetic/neural network learning for mimd shared-memory machines. IEEE Trans. Neural Netw, 5(6):900–909, 1994.
Harvey I. Cliff D. and Husbands P. Incremental evolution of neural network architectures for adaptive behaviour. In Proc. European Symp. Artif. Neural networks, pages 39–44, 1993.
R.S. Barto, A.G. Sutton and C.W. Anderson. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. on Syst., Man & Cybernetics, 13(5):834–846, 1983.
C. M. Fonseca and P. J. Fleming. An overview of evolutionary algorithms in multiob-jective optimization. Evolutionary Computation, 3(1):1–16, 1995.
Mamdani E.H. Applications of fuzzy logic for control of a simple dynamic process. Proc. IEE, 121:1585–1588, 1974.
Takagi H. Fusion techniques of fuzzy systems and neural networks and fuzzy systems and genetic algorithms. Proc. SPIE, 2061:402–413, 1993.
Antsaklis P. Defining intelligent control-report of the task force on intelligent control. IEEE Control Syst. Mag., page 4 et seq, June 1994.
Moore C.G. Harris C.J. and Brown M., editors. Intelligent Control: aspects of fuzzy logic and neural networks. World Scientific, 1993.
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Linkens, D.A., Nyongesa, H.O. (1997). Evolutionary Learning in Neural Fuzzy Control Systems. In: Pedrycz, W. (eds) Fuzzy Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6135-4_9
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DOI: https://doi.org/10.1007/978-1-4615-6135-4_9
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