Fuzzy Sets in Engineering Design and ConfigurationAs understanding of the engineering design and configuration processes grows, the recognition that these processes intrinsically involve imprecise information is also growing. This book collects some of the most recent work in the area of representation and manipulation of imprecise information during the syn thesis of new designs and selection of configurations. These authors all utilize the mathematics of fuzzy sets to represent information that has not-yet been reduced to precise descriptions, and in most cases also use the mathematics of probability to represent more traditional stochastic uncertainties such as un controlled manufacturing variations, etc. These advances form the nucleus of new formal methods to solve design, configuration, and concurrent engineering problems. Hans-Jurgen Sebastian Aachen, Germany Erik K. Antonsson Pasadena, California ACKNOWLEDGMENTS We wish to thank H.-J. Zimmermann for inviting us to write this book. We are also grateful to him for many discussions about this new field Fuzzy Engineering Design which have been very stimulating. We wish to thank our collaborators in particular: B. Funke, M. Tharigen, K. Miiller, S. Jarvinen, T. Goudarzi-Pour, and T. Kriese in Aachen who worked in the PROKON project and who elaborated some of the results presented in the book. We also wish to thank Michael J. Scott for providing invaluable editorial assis tance. Finally, the book would not have been possible without the many contributions and suggestions of Alex Greene of Kluwer Academic Publishers. 1 MODELING IMPRECISION IN ENGINEERING DESIGN Erik K. Antonsson, Ph.D., P.E. |
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Página 173
With the extension " linguistic variables " it is possible to use " linguistic terms " (
terms of the linguistic variable ) instead of crisp values . This concept is now
introduced by a simple example Example 1 ( def - do : name car : superconcept ...
With the extension " linguistic variables " it is possible to use " linguistic terms " (
terms of the linguistic variable ) instead of crisp values . This concept is now
introduced by a simple example Example 1 ( def - do : name car : superconcept ...
Página 174
5 bad medium good 100 style 40 60 80 Figure 42 : Linguistic variable " Style " ( of
object car ) in [ O 100 ) , a subinterval of this ... If we use linguistic variables to
characterize attributes , then we can use a - cuts to describe the specialization .
5 bad medium good 100 style 40 60 80 Figure 42 : Linguistic variable " Style " ( of
object car ) in [ O 100 ) , a subinterval of this ... If we use linguistic variables to
characterize attributes , then we can use a - cuts to describe the specialization .
Página 177
The idea is now that in addition to the parameters in Table 4 there are
parameters defined in the domain object car , which are linguistic variables and
have to be user defined . In this example we choose luggage - space and safety
to be ...
The idea is now that in addition to the parameters in Table 4 there are
parameters defined in the domain object car , which are linguistic variables and
have to be user defined . In this example we choose luggage - space and safety
to be ...
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Índice
o cr A CON Engineering Design with Imprecision | 10 |
Chapter 3 | 20 |
Chapter 2 | 48 |
Direitos de autor | |
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Outras edições - Ver tudo
Fuzzy Sets in Engineering Design and Configuration Hans-Jürgen Sebastian,Erik K. Antonsson Pré-visualização limitada - 2012 |
Fuzzy Sets in Engineering Design and Configuration Hans-Jürgen Sebastian,Erik K. Antonsson Pré-visualização indisponível - 2011 |
Fuzzy Sets in Engineering Design and Configuration Hans-Jurgen Sebastian,Erik K Antonsson Pré-visualização indisponível - 1996 |
Palavras e frases frequentes
Aachen additional aggregation algorithm allows alternatives applied approach assessment attributes base brake called combined completely components computed concepts configuration consider consistent constraints cost crisp criteria decision defined denotes described design variables determined developed domain Engineering engineering design evaluation example feasible Figure formulation fuzzy constraints fuzzy goals fuzzy sets given goals hierarchy illustrate imprecise individual induced integral introduced knowledge known KONWERK layout linguistic linguistic variables maximal means measure membership functions method minimization modules necessary objective functions obtained operator optimization optimum overall parameter Pareto-optimal performance performance variable possible preference presented problem production relations represented requirements Research respect restrictions selection shown solution solve space specification stage step strategy structure Table task techniques temperature theory uncertain uncertainty values weighting