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Adaptive neurofuzzy inference systems networks design using hybrid genetic and singular value decomposition methods for modeling and prediction of the explosive cutting process

Published online by Cambridge University Press:  01 November 2003

N. NARIMAN–ZADEH ,
A. DARVIZEH  and
M.H. DADFARMAI
N. NARIMAN–ZADEH
Affiliation:
Department of Mechanical Engineering, Engineering Faculty, Guilan University, Rasht, Iran
A. DARVIZEH
Affiliation:
Department of Mechanical Engineering, Engineering Faculty, Guilan University, Rasht, Iran
M.H. DADFARMAI
Affiliation:
Department of Mechanical Engineering, Engineering Faculty, Guilan University, Rasht, Iran
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Abstract

Genetic algorithm (GA) and singular value decomposition (SVD) are deployed for the optimal design of both Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of adaptive neurofuzzy inference systems (ANFIS) networks that are used for modeling of the explosive cutting process of plates by shaped charges. The aim of such modeling is to show how the depth of penetration varies with the variation of important parameters, namely, the apex angle, standoff, liner thickness, and mass of charge. It is demonstrated that SVD can be effectively used to optimally find the vector of linear coefficients of conclusion parts in ANFIS models and their Gaussian membership functions in premise parts are determined by a GA.

Keywords

Adaptive Neurofuzzy Inference SystemsExplosive Cutting ProcessGenetic AlgorithmsSingular Value Decomposition
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 4 , November 2003 , pp. 313 - 324
Copyright
2003 Cambridge University Press

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References

REFERENCES

Astrom, K.J. & Eykhoff, P. (1971). System identification, a survey. Automatica, 7, 123–162.CrossRefGoogle Scholar
Cheezari, M. (1999). Explosive separation using shaped charges. MS Dissertation. Guilan University.
Cordon, O., Herrera, F., Hoffmann, F., & Magdalena, L. (2001). In Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy knowledge Bases. Advances in Fuzzy Systems. Riveredge, NJ: World Scientific.CrossRef
Darvizeh, A., Nariman–Zadeh, N., & Gharababei, H. (2001). GMDH-type neural network modelling of explosive cutting process of plates using singular value decomposition. Proc. ESM'2001.
Delgado, M.R., Von Zuben, & F., Gomide F. (2001). Hierarchical genetic fuzzy systems. Information Sciences, 136, 29–52.CrossRefGoogle Scholar
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. New York: Addison–Wesley.
Golub, G.H. & Reinsch, C. (1970). Singular value decomposition and least squares solutions. Numerical Mathematics, 14(5), 403–420.CrossRefGoogle Scholar
Hoffmann, F. & Nelles, O. (2001). Genetic programming for model selection of TSK-fuzzy systems. Information Sciences, 136, 7–28.CrossRefGoogle Scholar
Iba, H., Kuita, T., deGaris, H., & Sator, T. (1993). System identification using structured genetic algorithms. Proc. Fifth Int. Conf. Genetic Algorithms, ICGA'93.
Jang, J.-S.R., Sun, C.-T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing: A Computation Approach to Learning and Machine Intelligence. Englewood Cliffs, NJ: Prentice Hall.
Jang, J.-S.R. & Mizutani, E. (1996). Levenberg–Marquardt method for ANFIS learning. Proc. Int. Joint Conf. North American Fuzzy Information Processing Society, Biannual Conf.
Jang, J.-S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665–685.CrossRefGoogle Scholar
Karr, C.L. (1991). Applying genetics to fuzzy logic. AI Expert, 6, 38–43.Google Scholar
Kosko, B. (1994). Fuzzy systems as universal approximator. IEEE Transactions on Computers, 43(11), 1324–1333.CrossRefGoogle Scholar
Koza, J. (1992). Genetic Programming, on the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.
Kristinson, K. & Dumont, G. (1992). System identification and control using genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 22(5), 1033–1046.Google Scholar
Lee, C.C. (1990). Fuzzy logic in control systems: fuzzy logic controller. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 404–434.CrossRefGoogle Scholar
Mannle, M. (2001). FTSM-Fast Takagi–Sugeno Fuzzy Modelling. Karlsruhe, Germany: Institute for Computer Design and Fault Tolerance, University of Karlsruhe, Germany.
Mannle, M. (1999). Identifying rule-based TSK fuzzy models. Proc. Seventh Eur. Congr. Intelligent Techniques Soft Computing, EUFIT'99.
Nariman–Zadeh, N. & Darvizeh, A. (2001). Design of fuzzy systems for the modelling of explosive cutting process of plates using singular value decomposition. Proc. WSES 2001 Conf. Fuzzy Sets Fuzzy Systems, FSFS'01.
Nariman–Zadeh, N., Darvizeh, A., & Oliaei, F. (2002). Genetic algorithm and singular value decomposition in the design of fuzzy systems for the modelling of explosive cutting process. Proc. WSES Conf. Fuzzy Sets Fuzzy Systems, FSFS '02.
Nemes, A. & Lantos, B. (1999). Genetic algorithm based optimization of fuzzy logic systems for dynamic modelling of robots. Periodica Polytechnica Electrical Engineering, 43(33), 177–187.Google Scholar
Porter, B. & Nariman–Zadeh, N. (1994). Genetic design of computed-torque controllers for robotic manipulators. Proc. IASTED Int. Conf. Systems Controllers.
Porter, B. & Nariman–Zadeh, N. (1995). Genetic design of computed-torque/fuzzy-logic controllers for robotic manipulators. Proc. IEEE Int. Symp. Intelligent Control.
Porter, B. & Nariman–Zadeh, N. (1997). Evolutionary design of fuzzy-logic controllers for manufacturing systems. Annals of the CIRP, 46(1).Google Scholar
Press, W.H., Teukolsky, S.A., Vetterling, W.T., & Flannery, B.P. (1992). Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd ed., New York: Cambridge University Press.
Rizzi, A., Mascioli, F.M.F., & Martinelli, G. (1999). Automatic training of ANFIS networks. Proc. IEEE Int. Fuzzy Systems Conf.CrossRef
Salem, S.A. & Al-Hassani, S.T.S. (1983). Penetration by high speed oblique jets: theory and experiments. International Journal of Mechanical Science, 25(12).CrossRefGoogle Scholar
Sanchez, E., Shibata, T., & Zadeh, L.A. (1997). Genetic Algorithms and Fuzzy Logic Systems. Riveredge, NJ: World Scientific.CrossRef
Setnes, M. & Roubos, J.A. (1999). Transparent fuzzy modeling using fuzzy clustering and GA's. Proc. NAFIPS, pp. 198–202.
Sugeno, M. & Kang, G.T. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28, 15–33.CrossRefGoogle Scholar
Takagi, T. & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, 116–132.CrossRefGoogle Scholar
Wang, L. & Yen, J. (1999). Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman filter. Fuzzy Sets and Systems, 101, 353–362.CrossRefGoogle Scholar
Wang, L., Langari, R., & Yen, J. (1999). Identifying fuzzy rule-based models using orthogonal transformation and backpropagation. In Fuzzy Theory Systems: Techniques and Application (Leondes, C.T., Ed.), Vol. 1. New York: Academic Press.
Wang, L.X. (1992). Fuzzy systems are universal approximators. Proc. IEEE Int. Conf. Fuzzy Systems, pp. 1163–1170.CrossRef
Zeng, X.J. & Singh, M.G. (1994). Approximation theory for fuzzy systems—SISO case. IEEE Transactions on Fuzzy Systems, 20(2), 162–176.CrossRefGoogle Scholar