The idea of soft computing emerged in the early 1990s from the fuzzy systems c- munity, and refers to an understanding that the uncertainty, imprecision and ig- rance present in a problem should be explicitly represented and possibly even - ploited rather than either eliminated or ignored in computations. For instance, Zadeh de?ned aSoft Computinga as follows: Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. Recently soft computing has, to some extent, become synonymous with a hybrid approach combining AI techniques including fuzzy systems, neural networks, and biologically inspired methods such as genetic algorithms. Here, however, we adopt a more straightforward de?nition consistent with the original concept. Hence, soft methods are understood as those uncertainty formalisms not part of mainstream s- tistics and probability theory which have typically been developed within the AI and decisionanalysiscommunity.Thesearemathematicallysounduncertaintymodelling methodologies which are complementary to conventional statistics and probability theory.We usually say a#39;John is tall and stronga#39; but not a#39;John is exactly 1.85 meters in height and he can lift 100kg weightsa#39;. ... We may notice that labels are used in natural language to describe what we see, hear and feel. ... and the corresponding appropriateness degree for using a a Current Address: Berkeley Initiative in Soft Computing, Electrical Engineering and Computer Sciences Department, University ofanbsp;...
|Title||:||Soft Methods for Integrated Uncertainty Modelling|
|Author||:||Jonathan Lawry, Enrique Miranda, Alberto Bugarin, Shoumei Li, Maria Angeles Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz|
|Publisher||:||Springer Science & Business Media - 2007-10-08|