An Adaptive Fuzzy Clustering Approach for the Network Management

The Chiu-s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering principle to estimate both the number of clusters and the initial locations of a cluster centers. Each obtained cluster corresponds to a Mamdani fuzzy rule. Then, it optimizes the fuzzy model parameters by applying a genetic algorithm. This method is illustrated on a traffic network management application. We suggest also a Mamdani fuzzy rules generation method, where the expert wants to classify the output variables in some fuzzy predefined classes.





References:
[1] M. Bellafkih, B. Idrissi Kaitouni, F. Elhebil, M. Ramdani. Un système ├á
base de connaissances neuro-flou pour la gestion du trafic d-un réseau
local, MCSEAI'2002, Annaba- Algeria, 4-6 May 2002.
[2] B. Bouchon-Meunier, M. Ramdani, C. Marsala. Inductive learning and
fuzziness, Scientia Iranica, International Journal of Science and
Technology, volume 2, Number 4, pp. 289-298, 1996.
[3] E. Carvalho, A. Dias Belchior, J Neuman de Souza. A fuzzy logic
application applied to local area network management. Networking and
Information Systems Journal, volume 2, Number 3, Hermes 1999.
[4] S. L Chiu. Extracting fuzzy rules from data for function approximation
and pattern classification. Chapter 9 in Fuzzy Information Engineering:
a Guided Tour of applications, ed. D. Dubois, H. Prade, and R. Yager,
John Wiley & Sons, 1997.
[5] S. L Chiu. Fuzzy model identification based on cluster estimation. J. of
Intelligent and Fuzzy Systems 2, 267-278, 1994.
[6] A. Elmzabi, M. Bellafkih, M. Ramdani, K. Zeitouni. Conditional Fuzzy
Clustering with Adaptive Method. IPMU'04, 4-9 July 2004, Perugia -
Italie.
[7] A. Elmzabi, M. Bellafkih, M. Ramdani, F. Elhebil. Méthode adaptative
pour générer des règles floues: Application ├á la gestion du réseau.
Journal Marocain d-Automatique, d-Informatique et de Traitement de
Signal. Pages 37-45, Numéro Spécial CoPSTIC-03, November 2004.
[8] P. Y. Glorennec. Algorithmes d-apprentissage pour systèmes d-inférence
floue. HERMES Science Publications, Paris, 1999.
[9] D. E. Goldberg. Genetic Algorithms in search, optimization, and machine
learning. Addison-Wesley, 1989.
[10] J. H. Holland. Adaptation in Natural and Artificial Systems, The
University of Michigan Press, 1975.
[11] J.-S. R Jang. ANFIS : adaptive-network-based fuzzy inference system.
IEEE Trans. on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665-
685, May 1993.
[12] J. S. R. Jang, C. T. Sun, E. Mizutani. Neuro-Fuzzy and Soft Computing:
A Computational Approach to Learning and Machine Intelligence. 1997
by Prentice-Hall, Inc. Upper Saddle River, NJ 07458.
[13] R. Pytelkova. Modelling and control with neuro-fuzzy system. Helsinki
Summer School 2001.
[14] R.P. Quach, A. Dandrieux, G. Dray, D.W. Peason, G.Dusserre.
Modelling gas dispersion by subtractive clustering and fuzzy c-means.
IPMU, vol 3, pp 1328-1334, Madrid July 3-7, 2000.
[15] R. A. Resende, S. M. Rossi, A. Yamakami, L. H Bonani, E. Moschim.
Traffic engineering with MPLS using fuzzy logic for application in IP
networks. Fuzzy IEEE, St Louis-USA, 25-28 May 2003.
[16] T. Takagi, M. Sugeno. Fuzzy identification of systems and its
applications to modelling and control. IEEE Trans. On Systems, Man,
and Cybernitics, 15:116-132, 1985.
[17] R. Yager and D. Filev. Generation of fuzzy rules by mountain clustering.
J. Of Intelligent and Fuzzy Systems 2, 209-219, 1994.
[18] J Yao, M. Dash, S.T. Tan, H. Liu. Entropy-based fuzzy clustering and
fuzzy modeling. Fuzzy Sets and Systems 113:381-388, 2000.
[19] L. A. Zadeh. Probability measure of fuzzy events. J Math. Anal. App,
23:421-356, 1992.