Difference between revisions 709284681 and 709286140 on enwiki

{{Distinguish|soft microprocessor}}
{{Expert-subject|computer science|date=July 2009}}

In [[computer science]], '''soft computing''' is the use of inexact solutions to computationally hard tasks such as the solution of [[NP-complete]] problems, for which there is no known algorithm that can compute an exact solution in [[polynomial time]]. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and [[approximation]]. In effect, the role model for soft computing is the [[human]] [[mind]]. 

The principal constituents of Soft Computing (SC) are [[Fuzzy Llogic]] (FL), Neural Computing (NC), [[Evolutionary Ccomputation]] (EC),  Machine Learning (ML) and Probabilistic Reasoning (PR), with the latter subsuming belief networks, c[[Chaos theory]] and parts of learning theory. It is important to note that each one of this constituents work in tandem rather than in a competitive way, each of the partners contributes a distinct methodology for addressing problems in its domain. 

==Introduction==
Soft computing (SC) solutions are unpredictable, uncertain and between 0 and 1. Soft Computing became a formal area of study in Computer Science in the early 1990s.<ref>Zadeh, Lotfi A., "Fuzzy Logic, Neural Networks, and Soft Computing,"  Communication of the ACM, March 1994, Vol. 37 No. 3, pages 77-84.</ref>  Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in [[biology]], [[medicine]], (contracted; show full)* [http://www.softcomputing.es EUROPEAN CENTRE FOR SOFT COMPUTING]
* [http://www.helsinki.fi/~niskanen/bisc.html BISC SIG IN PHILOSOPHY OF SOFT COMPUTING]

{{Authority control}}
[[Category:Scientific modeling]]
[[Category:Artificial intelligence]]
[[Category:Semantic Web]]
[[Category:Soft computing]]