International Research journal of Management Science and Technology

  ISSN 2250 - 1959 (online) ISSN 2348 - 9367 (Print) New DOI : 10.32804/IRJMST

Impact Factor* - 6.2311


**Need Help in Content editing, Data Analysis.

Research Gateway

Adv For Editing Content

   No of Download : 133    Submit Your Rating     Cite This   Download        Certificate

KERNAL BASED INTEGRATION OF NEURAL NETWORK AND ANT COLONIZATION ALGORITHM FOR EASY OPTIMIZATION OF TWO DIMENSIONAL DIAGRAMS

    2 Author(s):  DR. G. S. KATKAR, V.R. NIKAM

Vol -  9, Issue- 3 ,         Page(s) : 266 - 291  (2018 ) DOI : https://doi.org/10.32804/IRJMST

Abstract

This paper evaluating the critical fundamental of pattern and object detection technique, with full scale integration of kernel. Totally integrated separate result of ant colonization algorithm and neural network. The cross correlation data have histogram data, it also have kernel for our test image; integrating all this in one we have developed the full scale model for ACO and NN technique. It has calculated the error bound for this data, as well as we have checked the efficiency of this algorithm in terms of speed of computation, efficiency of memory allocation and pixel definition. Keywords : Neural Network, Kernel, canvolisation

  1. Dorigo.M,Birattari.M,Stützle.T,Ant Colony Optimization:Artificial Ants as a Computational Intelligence Technique,IEEE Computational Intelligence Magazine,(11);28-29.(2006)
  2. M. Fisher. Vehicle routing. Handbooks of Operations Research and Management Science, Chapter 1, 8:1-31,1995
  3. Sergios Theodoridis, Konstantinos Kourtoumbas. Pattern Recognition Second Edition page 582
  4. M.Gendreau, G. Laprte, and J-Y. Potvin. Metaheuristics for the vehicle routing problem. Management Science, 40:1276-1290,1994.
  5. G. Laporte, M. Gendreuau, J-Y. Potvin, and F. Semet. Classical and modern heuristics for the vehicle routing problem. International Transactions in Operational Research, 7:285-300,2000
  6. G. Laporte and F. Semet. Classical heuristics for the vehicle routing problem. Technical Report G-98-54, GERAD, 1999.
  7. Croes, G.: A method for solving traveling salesman problems. Operations Research 6. 791-812.
  8. Specht, D. F. (1990a) Probabilistic neural networks. Neural Networks 3, 109–118.
  9. Specht, D. F. (1990b) Probabilistic neural networks and the polynomial adaline as complementary techniques for classification. Transactions of the IEEE on Neural Networks 1, 111–121.
  10. Kohonen, T. (1988) Learning vector quantization. Neural Networks 1(suppl 1), 303.
  11. Kohonen, T. (1990) The self-organizing map. Proceedings of the IEEE 78, 1464–1480.
  12. Kohonen, T. (1995) Self-Organizing Maps. Berlin: Springer.
  13. [12]Ripley, B. D. (1994a) Flexible non-linear approaches to classification. In Cherkassky et al. (1994), pp. 105–126.
  14. Ripley, B. D. (1994b) Neural networks and flexible regression and discrimination. In Advances in Applied Statistics, ed. K. V. Mardia, pp. 39–Abingdon. Carfax.
  15. Ripley, B. D. (1994c) Neural networks and related methods for classification (with discussion). Journal of the Royal Statistical Society series B 56, 409–456.
  16. Ripley, B. D. (1995) Statistical ideas for selecting network architectures. In Neural Networks: Artificial Intelligence and Industrial Applications, eds. B. Kappen & S. Gielen, pp. 183–190, London. Springer.
  17. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press. ISBN 0-521-46086-7.
  18. Angluin, D. (1993) Learning with queries. In Computational Learning and Cognition, ed. E. B. Baum, pp. 1–28, Philadelphia. SIAM.
  19. Aitchison, J. & Dunsmore, I. R. (1975) Statistical Prediction Analysis. Cambridge University Press, Cambridge\
  20. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, UK: Cambridge Press, University 2000

*Contents are provided by Authors of articles. Please contact us if you having any query.






Bank Details