The Ordered Weighted Averaging Operators Theory and Applications

Aggregation plays a central role in many of the technological tasks we are faced with. The importance of this process will become even greater as we move more and more toward becoming an information-cent.ered society, us is happening with the rapid growth of the Internet and the World Wirle Weh. Here we shall be faced with many issues related to the fusion of information. One very pressing issue here is the development of mechanisms to help search for information, a problem that clearly has a strong aggregation-related component. More generally, in order to model the sophisticated ways in which human beings process information, as well as going beyond the human capa bilities, we need provide a basket of aggregation tools. The centrality of aggregation in human thought can be be very clearly seen by looking at neural networks, a technology motivated by modeling the human brain. One can see that the basic operations involved in these networks are learning and aggregation. The Ordered Weighted Averaging (OWA) operators provide a parameter ized family of aggregation operators which include many of the well-known operators such as the maximum, minimum and the simple average.

Product details

• Assisted by: Ronald R. Yager (Editor), Janusz Kacprzyk (Editor), J. Kacprzyk (Editor), Kacprzyk (Editor), Ronal R Yager (Editor), Kacprzyk (Editor), Ronald R Yager (Editor)
• Authors: Ronald R. Yager
• Publisher: Springer, Berlin
• Content: Book
• Product form: Paperback / Softback
• Publication date: 30.01.2013
• Subject: Natural sciences, medicine, IT, technology > Mathematics > Basic principles
• EAN: 9781461378068
• ISBN: 978-1-4613-7806-8
• Pages: 347
• Illustrations: X, 347 p.
• Dimensions (packing): 15.5 x 1.9 x 23.5 cm
• Weight (packing): 551 g
• Subjects: Stochastik, TOOLS, Algorithms, Wirtschaftsmathematik und -informatik, IT-Management, Wahrscheinlichkeitsrechnung und Statistik, Classification, Learning, Künstliche Intelligenz (KI), Fuzzy, Uncertainty, machinelearning, geneticalgorithms, fuzzylogic