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JACIII Vol.11 No.2 pp. 220-231
doi: 10.20965/jaciii.2007.p0220
(2007)

Paper:

Genetic and Bacterial Programming for B-Spline Neural Networks Design

János Botzheim*, Cristiano Cabrita**, László T. Kóczy*,***,
and Antonio E. Ruano**

*Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Magyar Tudósok Krt. 2, Budapest, H-1117, Hungary

**Centre for Intelligent Systems, University of Algarve, Campus de Gambelas, 8000 Faro, Portugal

***Institute of Information Technology and Electrical Engineering, Széchenyi István University, Győr, Hungary

Received:
June 21, 2006
Accepted:
October 17, 2006
Published:
February 20, 2007
Keywords:
constructive algorithms, B-splines, bacterial programming, genetic programming
Abstract

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

Cite this article as:
J. Botzheim, C. Cabrita, L. Kóczy, and <. Ruano, “Genetic and Bacterial Programming for B-Spline Neural Networks Design,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.2, pp. 220-231, 2007.
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References
  1. [1] C. Bishop, “Improving the generalization properties of radial basis function neural networks,” Neural Computation, Vol.3, pp. 579-588, 1991.
  2. [2] J. Botzheim, B. Hámori, L. T. Kóczy, and A. E. Ruano, “Bacterial algorithm applied for fuzzy rule extraction,” in Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, IPMU 2002, pp. 1021-1026, Annecy, France, 2002.
  3. [3] M. Brown and C. Harris, “Neurofuzzy Adaptive Modelling and Control,” Prentice-Hall, 1994.
  4. [4] C. Cabrita, A. E. Ruano, and C. M. Fonseca, “Single and multiobjective genetic programming design for B-spline neural networks and neuro-fuzzy systems,” IFAC Workshop on Advanced Fuzzy/Neural Control (AFNC’01), pp. 93-98, Valencia, Spain, 2001.
  5. [5] C. Cabrita, J. Botzheim, A. E. Ruano, and L. T. Kóczy, “Genetic programming and bacterial algorithm for neural networks and fuzzy systems design,” IFAC International Conference on Intelligent Control Systems and Signal Processing (ICONS 2003), pp. 500-505, Faro, Portugal, 2003.
  6. [6] C. Cabrita, J. Botzheim, A. E. Ruano, and L. T. Kóczy, “Design of B-spline Neural Networks using a Bacterial Programming Approach,” International Joint Conference on Neural Networks, pp. 2313-2318, Budapest, Hungary, 2004.
  7. [7] J. H. Friedman, “Multivariate Adaptive Regression Splines,” The Annals of Statistics, Vol.19, No.1, pp. 1-141, 1991.
  8. [8] D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning,” Addison-Wesley, Reading, Massachusetts, 1989.
  9. [9] J. H. Holland, “Adaptation in natural and artificial systems,” University of Michigan Press, Ann Arbor, USA, 1975.
  10. [10] L. T. Kóczy and A. Zorat, “Fuzzy systems and approximation,” Fuzzy Sets and Systems, Vol.85, pp. 203-222, 1995.
  11. [11] J. R. Koza, “Genetic Programming: On the Programming of Computers by Means of Natural Selection,” MIT Press, 1992.
  12. [12] J. R. Koza, “Genetic Programming II, Automatic Discovery of Reusable Programs,” 2nd ed., MIT, 1998.
  13. [13] H. B. Mann and D. R. Whitney, “On a test of whether one of two random variables is stochastically larger than the other,” Annals of Mathematical Statistics, Vol.18, pp. 50-60, 1947.
  14. [14] N. E. Nawa and T. Furuhashi, “Fuzzy System Parameters Discovery by Bacterial Evolutionary Algorithm,” IEEE Tr. Fuzzy Systems 7, pp. 608-616, 1999.
  15. [15] N. E. Nawa, T. Hashiyama, T. Furuhashi, and Y. Uchikawa, “Fuzzy logic controllers generated by pseudo-bacterial genetic algorithm,” in Proc. IEEE 1997 Int. Conf. Neural Networks (ICNN’97), Houston, pp. 2408-2413, 1997.
  16. [16] O. Nelles, “Nonlinear Systems Identification with Local Linear Neuro-Fuzzy Models,” Ph.D. Thesis, TU Darmstadt, Germany, 2000.
  17. [17] A. E. Ruano, C. Cabrita, J. V. Oliveira, and L. T. Kóczy, “Supervised training algorithms for B-spline neural networks and neurofuzzy systems,” International Journal of Systems Science, Vol.33, No.8, pp. 689-711, 2002.
  18. [18] E. Weyer and T. Kavli, “The ASMOD Algorithm. Some New Theoretical and Experimental Results,” SINTEF Report STF31 A95024, Oslo, 1995.
  19. [19] K. F. C. Yiu, S. Wang, K. L. Teo, and A. C. Tsoi, “Nonlinear System Modeling via Knot-Optimizing B-Spline Networks,” IEEE Trans. Neural Networks, Vol.12, pp. 1013-1022, 2001.

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Last updated on Mar. 14, 2019