A genetic-designed beta basis function neural network for multi-variable functions approximation

Systems Analysis Modelling Simulation

Volumn 42, Issue 7, 2002, Pages 975-1009

  Aouiti, Chaouki ^a   Alimi, Adel M ^b   Maalej, Aref ^b  

^a UNIVERSITY OF CARTHAGE   (Tunisia)

^b UNIVERSITY OF SFAX   (Tunisia)

Author keywords

Beta function; Functions approximation; Genetic algorithms; Learning; Neural networks

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; FUNCTIONS; MATHEMATICAL MODELS; MULTIVARIABLE CONTROL SYSTEMS; PARAMETER ESTIMATION;

BETA FUNCTION; FUNCTIONS APPROXIMATION; GENETIC OPERATORS; LEARNING;

NEURAL NETWORKS;

EID: 10444235281     PISSN: 02329298     EISSN: 10294902     Source Type: Journal    
DOI: 10.1080/716067203     Document Type: Article

References (35)

1. A.M. Alimi (June 2000). The beta system: toward a change in our use of neuro-fuzzy systems. Management, pp. 15-19, Invited paper.
2. A.M. Alimi, R. Hassine and M. Selmi (2000). Beta fuzzy logic systems: approximation properties in the SISO case. Int. J. Applied Mathematics and Computer Science, Special issue edited by D. Rutkowska and L.A. Zadeh, 10(4), 101-119.
3. M.A. Alimi (April 1998). What are the advantages of using the Beta neuro-fuzzy system? Proc. IEEE/IMACS Multiconference on Computational Engineering in Systems Applications: CESA' 98, Vol. 2. pp. 339-344. Hammamet, Tunisia.
4. M.A. Alimi (November 1997a). The beta fuzzy system: approximation of standard membership functions. Proc. 17eme Journees Tunisiennes d'Electrotechnique et d'Automatique: JTEA' 97, Vol. 1. pp. 108-112. Nabeul, Tunisia.
5. M.A. Alimi (February 1997b). Beta fuzzy basis functions for the design of universal robust neuro-fuzzy controllers. Proc. Seminaire sur la Commande Robuste and ses Applications: SCRA'97, pp. C1-C5. Nabeul, Tunisia.
6. C. Aouiti, M.A. Alimi and A. Maalej (April 2001). A genetic designed Beta basis function neural networks for approximating of multi-variables functions. Proc. Int. Conf. Artificial Neural Nets and Genetic Algorithms Springer Computer Science: ICANNGA' 2001, pp. 383-386. Prague, Czzch Republic.
7. C. Aouiti, M.A. Alimi and A. Maalej (March 2000). Genetic algorithms to construct Beta neuro-fuzzy systems. Proc. Int. Conf. Artifical & Computational Intelligence for Decision, Control and Automation; ACIDCA'2000, pp. 88-93. Monastir, Tunisia.
8. S.A. Billings and G.L. Zheng (1995). Radial basis function network configuration using genetic algorithms. Neural Networks, 8(6), 877-890.
9. E.J.W. Boers and H. Kuiper (1992). Biological metaphors and the desing of modular artificial neural networks. Unpublished Master's thesis, Leiden University.
10. P.A. Castillo, V. Rivas, J.J. Merelo, J. Gonzalez, A. Prieto and G. Romero (1999). G- Prop- II: Global Optimization of Multilayer Perceptrons using G As, pp. 2022-2027.
11. L.J. Eshelman, R. Caruna and J.D. Schaffer (1989). Biases in the crossover landscape. In: J.D. Schaffcr (Ed.), Proceedings of the Third International Conference on Genetic Algorithms, pp. 10-19. Morgan Kaufmann.
12. L.J. Fogel, A.J. Owens and M.J. Walsh (1966). Artificial Intelligence Through Simulated Evolution, Wiley, New York.
13. D.E. Goldberg (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.Reading, MA.
14. J.H. Holland (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Harbor.
15. K.J. Hunt, R. Haas and R. Murray-Smith (1996). Extended the functional equivalence of radial basis functions networks and fuzzy inference systems. IEEE Trans. Neural Networks, 7, 776-781.
16. S. Haykin (1994). Neural Networks. A Comprehensive Foundation. Macmillan College Publishing Company, New York.
17. T. Holcomb and M. Morari (1991). Location training for radial basis function networks: towards solving the hidden unit problem. Proc. Amer. Control Conf., pp. 2331-2336.
18. D. Hiratsu and T. Nagao (1998). Automatic generation of adequate parameters in artificial cellular neural networks for optimization with genetic algorithm. ICIS' 98 August, pp. 2331-2336, Australia.
19. J.S.R. Jang and C.T. Sun (1993). Functional equivalence between radial basis functions networks and fuzzy inference systems. IEEE Trans. Neural Networks, 4, 156-158.
20. D.J. Jonson and J.F. Frenzel (1992). Application of genetic algorithms to the training of higher order neural networks. Journal of Systems Engineering, 2, 272-276. Springer-Verlag London Limited.
21. S. Lee and M.K. Rhee (1991). A gaussian potential function networks with hierarchically self-organizing learning. Neural Networks, 4, 207-224.
22. W.A. Light (1992). Some aspects of radial basis function approximation. Approx, Theory, Spline Functions Applic., 356, 163-190.
23. E.P. Maillard and D. Gueriot (April 1998). A biased genetic algorithm applied to neural network design. Proc. CESA' 98, 4, 23-27. Hammamet.
24. M. Masmoudi, M. Samet and M.A. Alimi (2000). A bipolar implementation of the Beta neuron. International Journal of Electronics, in press.
25. S. Mitaim and B. Kosko (1996). what is the best shape for fuzzy set in function approximation? IEEE, 3, 1237-1243.
26. M.T. Musavi, W. Ahmed, K.H. Chan, B.K. Faris and D.M. Hummels (1992). On the training of radial basis function classifiers. Neural Networks, 5, 595-603.
27. M.J.D. Powell (1992). Radial basis function in 1990. Adv. Number Anal., 2, 105-210.
28. A. Rahmouni and M. Benmohamed (November 1998). Genetic algorithm based methodology to generate automatically optimal fuzzy system. IEE Proc-Control Theory Appl., 145 (6), 583-586.
29. P. Samuel and L. Gisele (1998). A genetic algorithm for designing distributed computer network topologies. IEEE Trans, Systems, Man, and Cybernetics-Part B: Cybernetics, 28(2), 249-258.
30. T.L. Seng, M. Bin Khalid and R. Yusof (April 1999). Tuning of a neurro-fuzzy controller by genetic algorithm. IEEE Transactions on systems. Man and cybernetics - Part B, 29(2), 226-236.
31. L.X. Wang and J.M. Mendel (1992). Fuzzy basis functions, universal approximations, and orthogonal least-squares learning. IEEE Trans. Neural Networks, 3, 807-814.
32. B.A. Whitechead and T.D. Choatc (1996). Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Trans Neural Networks, 7(4), 869-880.
33. X. Yao (1993). Evolving artificial neural networks. Int. J. Neural Systems, 4(3), 203-222.
34. X. Yao and Y. Liu (1997). A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks, 8(3), 694-713.
35. X. Yao (1999). Evolving artificial neural networks. IEEE Proc. Computational Intelligence, 87(9), 1423-1447.