Abstract
There are many situations where imprecise or incomplete information is available about a problem until an approximate solution of the problem is obtained. To give a simple example, in most of the mechanics problem, one has to use a coefficient of friction whose value is not known precisely. While one can measure the mass of the body quite easily and values of applied forces may be known, the coefficient of friction may not be known precisely. For example Merium and Kraige [1] give a table of friction for various contacting surfaces, but mention that a variation of 25–100% or more from those values could be expected in an actual application, depending on prevailing conditions of cleanliness, surface finish, pressure, lubrication, and velocity. Notwithstanding the prevalent imprecision in the values of coefficient of friction, most of the time one does carry out precise (hard) computations with the most likely value of the coefficient of friction. However, such hard computations without a mention of imprecision in the solution have limited practical utility.
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8.7 References
Merium, J.L. and Kraig, L.G. (1999), Engineering Mechanics, Vol.2, Dynamics, John Wiley & Sons (Asia), Singapore.
Zadeh, L.A. (1994), Fuzzy logic, neural networks and soft computing, Communications of the ACM, Vol. 3, No. 3, pp. 77–84.
Kosko, B. (1994), Fuzzy thinking: the New Science of Fuzzy Logic, Flamingo, London.
Zurada, J.M. (1997), Introduction to Artificial Neural Systems, Jaico Publishing House, Mumbai.
Hassoun, M.H. (1999), Fundamentals of Artificial Neural Networks, Prentice-Hall of India, New Delhi.
Ham, F.M. and Kostanic, I. (2001), Principles of Neurocomputing for Science and Engineering, McGraw-Hill, New York.
Haykin, S. (2004), Neural Networks: A Comprehensive Foundation, Prentice-Hall of India, New Delhi.
McCulloch, W.S. and Pitts W.H. (1943), A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, Vol. 5, pp. 115–133.
Tocci, R.J. (2000), Digital System: Principle and Applications, sixth edition, Prentice-Hall of India, New Delhi.
Rosenblatt, F., (1958), The perceptron: a probabilistic model for information storage in the brain, Psychological Review, Vol. 65, pp. 386–408.
McClelland, T.L. Rumelhart, D.E. and the PDP Research Group (1986), Parallel Distributed Processing, The MIT Press, Cambridge.
Shynk, J.J. (1990), Performance surfaces of a single-layer perceptron, IEEE Transactions on Neural Networks, Vol. 1, pp. 268–274.
Hebb, D. (1949), The Organization of Behavior, Wiley, New York.
Jang, J.-S.R., Sun, C.-T. and Mizutani, E. (2002), Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence, Prentice-Hall of India, New Delhi.
Zadeh, L.A. (1965), Fuzzy sets, Information and Control, Vol.8, pp.338–353.
Klier, G.J. and Folger, T.A. (1993), Fuzzy Sets, Uncertainty and Information, Prentice-Hall of India Private Limited, New Delhi.
Kaufmann A. and Gupta, M.M. (1985), Introduction to Fuzzy Arithmetic: Theory and Applications, Von Nostran Reinhold Company Inc., New York.
Bojadziev G. and Bojadziev, M. (1995), Advances in Fuzzy Systems-Application and Theory, Vol. 5, World Scientific, Singapore.
Zadeh, L.A. (1976), A fuzzy-algorithmic approach to the definition of complex or imprecise concepts, International Journal of Man-Machine Studies, Vol. 8, pp. 249–291.
Civanlar, M.R. and Trussel, H.J. (1986), Constructing membership functions using statistical data, Fuzzy Sets and Systems, Vol. 18, pp. 1–14.
Karr, C.L. (1991), Design of an adaptive fuzzy controller using a genetic algorithm, Proceedings of 4th International Conference on Genetic Algorithms, San Diego, CA, pp. 450–456.
Arslan, A. and Kaya, M. (2001), Determination of fuzzy logic membership function using genetic algorithms, Fuzzy Sets and Systems, Vol. 118, pp. 297–306.
Garibaldi, J.M. and Ifeachor, E.C. (1999), Application of simulated annealing fuzzy model tuning to umbilical cord acid-base interpretation, IEEE Transactions on Fuzzy Systems, Vol. 7, pp. 72–84.
Homaifar, A. and McCormic, E. (1995), Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms, IEEE Transactions on Fuzzy Systems, Vol. 3, pp. 129–139.
Bagis, A. (2003), Determination of fuzzy membership function with tabu search-an application to control, Fuzzy Sets and Systems, Vol. 139, pp. 209–225.
Zadeh, L.A. (1973), Outline of a new approach to the analysis of complex systems and decision processes, Information Science, Vol. 9, pp.43–80.
Mamdani, E.H. (1976), Advances in linguistic synthesis of fuzzy controllers, International Journal of Man-Machine Studies, Vol. 8, pp.669–678.
Takagi, T. and Sugeno, M. (1985), Fuzzy identification of systems and its applications to modeling and control, IEEE transactions on System, Man and Cybernetics, Vol. 15, pp. 116–132.
Sugeno, M. and Kang, G.T. (1988), Structure identification of fuzzy model, Fuzzy Sets and Systems, Vol. 28, pp. 15–33.
Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann arbor.
Deb, K. (2003), Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, Singapore.
Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
Goldberg, D.E., Deb K. and Clark, J.H. (1992), Genetic algorithms, noise, and the sizing of populations, Complex Systems, Vol. 6, pp. 333–362.
Grefenstette, J.J. (1986), Optimization of control parameters for genetic algorithms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 16, 122–128.
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(2008). Background on Soft Computing. In: Modeling of Metal Forming and Machining Processes. Engineering Materials and Processes. Springer, London. https://doi.org/10.1007/978-1-84800-189-3_8
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