Soft computing

Here are entered works on a group of technologies and methods, including knowledge-based systems, neural networks, fuzzy set theory, genetic algorithms, and probabilistic and evidential approaches, that mimic the human ability to make decisions in an environment of uncertainty and imprecision. Works on computing that intelligently trades off implementation, storage, and/or result accuracy for performance or energy gains are entered under [Approximate computing.]

found: Work cat.: 96-8559: International Workshop on Soft Computing(Soft computing is a new discipline rooted in a group of technologies, such as knowledge-based systems, neural networks, fuzzy set theory, probalistic and evidential approaches, and genetic algorithms)

found: TechTarget, What is IT web site, viewed Oct. 25, 2018(Soft computing is the use of approximate calculations to provide imprecise but usable solutions to complex computational problems. The approach enables solutions for problems that may be either unsolvable or just too time-consuming to solve with current hardware. Soft computing is sometimes referred to as computational intelligence. Soft computing provides an approach to problem-solving using means other than computers. With the human mind as a role model, soft computing is tolerant of partial truths, uncertainty, imprecision and approximation, unlike traditional computing models. The tolerance of soft computing allows researchers to approach some problems that traditional computing can't process)

found: TechTarget, What is IT web site, viewed Oct. 25, 2018(Cognitive computing: Cognitive computing systems use computerized models to simulate the human cognition process to find solutions in complex situations where the answers may be ambiguous and uncertain. While the term cognitive computing is often used interchangeably with artificial intelligence (AI), the phrase is closely associated with IBM's cognitive computer system, Watson)

found: A definition of soft computing, via WWW, June 8, 2020(Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation; The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost; the principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Neural Computing (NC), Evolutionary Computation (EC) Machine Learning (ML) and Probabilistic Reasoning (PR); soft computing may be viewed as a foundation component for the emerging field of conceptual intelligence)

Here are entered works on a group of technologies and methods, including knowledge-based systems, neural networks, fuzzy set theory, genetic algorithms, and probabilistic and evidential approaches, that mimic the human ability to make decisions in an environment of uncertainty and imprecision. Works on computing that intelligently trades off implementation, storage, and/or result accuracy for performance or energy gains are entered under [Approximate computing.]