Soft Methods for Integrated Uncertainty Modelling (2006) (Advances in Intelligent and Soft Computing #37)

Synopsis

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 ‘Soft Computing’ 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.

Copyright:

2006

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9783540347774

Related ISBNs:

9783540347767

Publisher:

Springer Berlin Heidelberg

Date of Addition:

07/17/22

Copyrighted By:

N/A

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Computers and Internet, Technology, Mathematics and Statistics

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

Jonathan Lawry

Edited by:

Enrique Miranda

Edited by:

Alberto Bugarin

Edited by:

Shoumei Li

Edited by:

Maria Angeles Gil

Edited by:

Przemyslaw Grzegorzewski

Edited by:

Olgierd Hryniewicz