A hybrid genetic fuzzy neural network algorithm designed for classification problems involving several groups

Fuzzy Sets and Systems

Volumn 114, Issue 2, 2000, Pages 311-324

  Östermark, Ralf ^a  

^a ÅBO AKADEMI UNIVERSITY   (Finland)

Author keywords

Evolutionary computation; Fuzzy set theory; Hybrid fuzzy neural networks; Multigroup classification

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA STRUCTURES; GENETIC ALGORITHMS; MEMBERSHIP FUNCTIONS; NEURAL NETWORKS;

EVOLUTIONARY COMPUTATIONS; GENETIC FUZZY NEURAL NETWORKS (GFNN);

FUZZY SETS;

EID: 0033722932     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(98)00057-8     Document Type: Article

References (37)

1. J. Bezdek, E.C.-K. Tsao, N. Pal, Fuzzy Kohonen clustering networks, Proc. IEEE Internat. Conf. on Fuzzy Systems, San Diego, 1992, pp. 1035-1043.
2. Buckley J.J., Hayashi Y. Fuzzy input-output controllers are universal approximators. Fuzzy Sets and Systems. 58:1993;273-278.
3. Buckley J.J., Hayashi Y., Czogala E. On the equivalence of neural nets and fuzzy expert systems. Fuzzy Sets and Systems. 53:1993;129-134.
4. E. Cantú-Paz, A summary of research on parallel genetic algorithms, IlliGAL Report No. 95007, July 1995, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign.
5. Th. Feuring, Neuronale Fuzzy-Netze - Eine Einführung, Westfälische Wilhems-Universität Münster, Institut für Numerische und Instrumentelle Mathematik/Informatik, Bericht Nr. 2/94-I.
6. Freed N., Glover F. Simple but powerful goal programming models for discriminant problems. European J. Oper. Res. 7:1981;44-60.
7. R. Fuller, Neural Fuzzy Systems, Institute for Advanced Management Systems Research, Åbo Akademi University, Ser. A:443, Åbo, Finland, 1995.
8. Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks. 2:1989;183-192.
9. W. Gochet, Srinivasan, A. Stam, S. Chen, Multigroup discriminant analysis using linear programming, Oper. Res. 45 (2) (1997).
10. Homaifar A., Lai S.H-Y., Qi X. Constrained optimization via genetic algorithms. Simulation. 62:1994;242-254.
11. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79:1982;2554-2558.
12. J. Horn, N. Nafphotis, Multiobjective optimization using the niched Pareto genetic algorithm, in: Proc. 1st IEEE Conf. on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 1, 1994 (ICEL'94), Piscataway, NJ, IEEE Service Center, pp. 82-87.
13. Hornik K., Stinchcombe M., White H. Multi-layer feedforward networks are universal approximators. Neural Networks. 2:1989;359-366.
14. Ishibuchi H., Nozaki K., Tanaka H. Efficient fuzzy partitions of pattern space for classification problems. Fuzzy Sets and Systems. 59:1993;295-304.
15. Ishibuchi H., Nozaki K., Yamamoto N., Tanaka H. Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms. Fuzzy Sets and Systems. 65:1994;237-253.
16. Ishibuchi H., Tanaka H. Approximate pattern classification using neural networks. Lowen R., Roubens M. Fuzzy Logic. 1993;225-236 Kluwer Academic Publishers, The Netherlands.
17. J. Joines, C. Houck, On the use of non-stationary penalty functions to solve nonlinear constrained optimization functions with GA's, in: Proc. Evolutionary Computation Conference - Poster Sessions, IEEE World Congress on Computational Intelligence, Orlando, 27-29 June 1994, pp. 579-584.
18. Juels, Wettenberg, Stochastic hillclimbing as a baseline method for evaluating GAs, in: NIPS8, MIT Press, Cambridge, 1996.
19. Kohonen T. Self-organization and Associative Memory. 1984;Springer, New York.
20. Kwan H., Cai X. A fuzzy neural network and its application to pattern recognition. IEEE Trans. Fuzzy Systems. 2(3):1994;186-193.
21. Lee H., Wang W. A neural network architecture for classification of fuzzy inputs. Fuzzy Sets and Systems. 63:1994;159-173.
22. W.M. Lippe, Th. Feuring, A. Jankrift, Klassifizierung neuro magnetischer Feldmuster unter Verwendung eines Cascade- Correlation Netzes, Westfälische Wilhems-Universität Münster, Institut für Numerische und Instrumentelle Mathematik/Informatik, Bericht Nr. 3/94-I.
23. Michalewicz Z. Genetic Algorithms+Data Structures=Evolution Programs, AI Series. 1993;Springer, New York.
24. Michalewicz Z. Evolutionary computation techniques for nonlinear programming problems. Internat. Trans. Oper. Res. 1(2):1994;223-240.
25. Michalewicz Z. Heuristic methods for evolutionary computation techniques. J. Heuristics. 1:1996;177-206.
26. Mitra S., Pal S. Self-organizing neural network as a fuzzy classifier. IEEE Trans. Systems, Management, Cybernet. 24(3):1994;385-399.
27. R. Östermark, A Flexible Genetic Hybrid Algorithm for Nonlinear Mixed-integer Programming Problems, Evolutionary Optimization, forthcoming.
28. R. Östermark, A fuzzy neural network algorithm for multigroup classification, Department of Business Administration, Åbo Akademi University, 1996b; Presented at ERUDIT - European Symposium on Intelligent Techniques in Bari, Italy, 20-22 March, 1997.
29. R. Östermark, R. Höglund, Addressing the multigroup discriminant problem using multivariate statistics and mathematical programming, European J. Oper. Res., forthcoming.
30. J. Richardson, M. Palmer, G. Liepins, M. Hilliard, Some guidelines for genetic algorithms with penalty functions, in: Proc. 3rd Internat. Conf. on Genetic Algorithms, Morgan Kaufmann, Los Altos, CA, 1989, pp. 191-192.
31. Rumelhart D.E., McClelland J.L. Parallel Distributed Processing. Explorations in the Microstructure of Cognition:1986;MIT Press/Bradford Books, Cambridge, MA.
32. W. Siedlicki, J. Sklanski, Constrained genetic optimization via dynamic reward-penalty balancing and its use in pattern recognition, in: Proc. 3rd Internat. Conf. on Genetic Algorithms, Morgan Kaufmann, Los Altos, CA, 1989, pp. 141-150.
33. Sprecher D.A. On the structure of continuous functions of several variables. Trans. Amer. Math. Soc. 115:1965;340-355.
34. Sugeno M., Park G.-K. An approach to linguistic instructions based learning. Internat. J. Uncertainty, Fuzziness Knowledge-based Systems. 1:1993;19-56.
35. Watanabe H.et al. Application of fuzzy discriminant analysis for diagnosis of valvural heart disease. IEEE Trans. Fuzzy Systems. 2:1994;267-276.
36. O. Wolpert (Ed.), The Mathematics of Generalization, Santa Fe Institute, Addison-Wesley, Reading, MA, 1995.
37. Zadeh L.A. Fuzzy sets. Inform. and Control. 8:1965;338-353.