Abstract
This chapter starts from the mathematical model of vagueness and imprecision originally proposed by Zadeh (1965) who suspected that an ever-increasing amount of precision in mathematical modelling would lead to almost insignificant models for control systems. Fuzzy-set theory experienced considerable resistance from probability theory, but in electrical engineering it is now widely accepted as a suitable model for the verbal classification of observations and control commands.
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References to Chapter 2
Bellman, R., and Giertz, M., “On the Analytic Formalism of the Theory of Fuzzy Sets”. Information Science 5, 149 – 156, 1973.
Dubois, D., and Prade, H., “Fuzzy Sets and Systems, Theory and Applications”. Academic Press, New York, 1980.
Kosko, B., “Neural Networks and Fuzzy Systems”. Prentice Hall, Englewood Cliffs, New Jersey, 1992.
Rosch, E., “Principles of Categorization”. In E. Rosch and B. Lloyds (eds.), “Cognition and Categorization”. Lawrence Erlbaum, Hillsdale, New Jersey, 1978, pp. 27 – 48.
Zadeh, LA., “Fuzzy Sets”. Information and Control 8, 338 – 353, 1965.
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© 1997 Springer Science+Business Media Dordrecht
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Lootsma, F.A. (1997). Basic Concepts of Fuzzy Logic. In: Fuzzy Logic for Planning and Decision Making. Applied Optimization, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2618-3_2
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DOI: https://doi.org/10.1007/978-1-4757-2618-3_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4779-6
Online ISBN: 978-1-4757-2618-3
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