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Published March 27, 2009 | Version 5810
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Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application

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Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. M-ANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying M-ANFIS to evaluate traffic Level of service show that M-ANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in non-linear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters.

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References

  • R. C. Eberhart, Overview of computational intelligence, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 20(3),1998.
  • L. A. Zadeh, Soft Computing and Fuzzy Logic, IEEE Software, 11(6):48-56, 1994.
  • J. S. R. Jang, ANFIS:Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions On Systems, Man, And Cybernetics ,23(3):665-685, 1993.
  • M. M. Gupta, Fuzzy Logic and Fuzzy Systems: Recent Developments and Future Diwctions, Intelligent Systems Research Laboratory, 1996.
  • M.A. Dena, F. Palis and A. Zeghbib, Modeling and control of nonlinear systems using soft computing techniques, Applied Soft Computing, 7:728-738,2007.
  • T. Furuhashi, Fusion of fuzzy/ neuro/ evolutionary computing for knowledge acquisition, Proceedings of the IEEE, 89(9),2001.
  • H. Takagi and I. Hayashi, Artificial neural network-driven fuzzy reasoning, Proc. Int. Workshop Fuzzy System Applications (IIZUKA88), Iizuka, Japan, 217C218,1988.
  • C. T. Lin and C. S. G. Lee, Neural-network-based fuzzy logic control and decision system, IEEE Transactions on computers, 40(12):1320- 1336,1991.
  • C. T. Lin and C. S. G. Lee, Reinforcement Structure/Parameter Learning for an Integrated Fuzzy Neural Network, IEEE Transactions on Fuzzy Systems, 2(1):46-63, 1994. [10] L.X.Wang and J.M. Mendel, Back-propagation fuzzy systems as nonlinear dynamic system identifiers, Proceedings of the IEEE International Conference on Fuzzy Systems, San Diego, March 1992. [11] L.X. Wang and J. M. Mendel, Fuzzy Basis Functions, Universal Approximation, and Orthogonal Least-Squares Learning, IEEE TRANSACTIONS ON NEURAL NETWORKS, 3(5):807-814,September 1992. [12] J.-S. R. Jang and C.-T. Sun, Functional equivalence between radial basis function networks and fuzzy inference systems, IEEE Transaction on Neural Networks 4(1)(1993)156-159. [13] J.-S. R. Jang, C.-T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, First Edition, Prentice Hall, 1997. [14] E.H.Mamdani and S.Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, 7(1):1-13,1975. [15] F. Esragh and E.H. Mamdani, A general approach to linguistic approximation, Fuzzy Reasoning and Its Applications, Academic Press,1981. [16] E. H. Mamdani, Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis, IEEE Trans. Computers, 26(12):1182- 1191, 1977. [17] T. Takagi and M. Sugeno, Derivation of fuzzy control rules from human operators control actions, Proc. IFAC Symp. on Fuzzy lnformation, Knowledge Representation and Decision Analysis, 55-60, July 1983. [18] T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst., Man, Cybern, 15:116-132, 1985. [19] R.R.Yager and D.P.Filev, SLIDE:A simple adaptive defuzzification method, IEEE transaction on Fuzzy Systems, 1(1):69-78,February 1992. [20] D. E. Rumelhart, G. E. Hinton and R. J. Williams, Learning internal representations by error propagation, Parallel distributed processing: explorations in the microstructure of cognition. 1: 318- 362,foundations, 1986. [21] D.E. Rumelhart, The Basic Ideas in Neural Networks, Communications of the ACM, 37(3):87-92,1994. [22] M.Minsky and S.Papert, Perceptrons, MIT Press, Cambridge, MA., 1969. [23] J. Moody and C. Darken, Learning with Localized Receptive Fields, Proceedings of the 1988 Connectionist Models Summer School, 133- 143, Morgan Kaufmann, 1988. [24] J.Moody and C.Darken, Fast learning in networks of locally-turned processing units, Neural Computation, 1:281-294,1989.