Soft Methods for Integrated Uncertainty Modelling

丛 书 名：Advances in Intelligent and Soft Computing

版本说明：1

作     者：Jonathan Lawry Enrique Miranda Alberto Bugarin Shoumei Li Maria Angeles Gil Przemys aw Grzegorzewski Olgierd Hyrniewicz 

I S B N：(纸本) 9783540347767 

出 版 社：Springer Berlin  Heidelberg 

出 版 年：2006年

页      数：X, 413页

主 题 词：Artificial Intelligence Mathematical and Computational Engineering Applications of Mathematics 

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

摘      要：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 *** methodologies which are complementary to conventional statistics and probability theory.