Difference between revisions 937164286 and 937164508 on enwiki

{{Distinguish|soft microprocessor}}

{{Multiple issues|
{{Expert needed|computer science|date=July 2009}}
{{Notability|date=August 2019}}
{{Essay-like|date=August 2019}}
{{Confusing|date=August 2019}}
{{Incomprehensible|date=August 2019}}
(contracted; show full)  In 'hard' computing, by contrast, machines need precision, determinism and  measures, and although pattern recognition happens, there is a 'brittleness' if things change - it cannot easily adapt. 'Soft' computing by contrast embraces chaotic, neural models of computing that are more pliable.  Because there is no known single method that lets us compute like people,  soft computing involves using a combination of methods that each bring something helpful to achieve this goal.
 The principal constituents of Soft Computing (SC) are [[Fuzzy logic|Fuzzy Logic]] (FL), [[Evolutionary computation|Evolutionary Computation]] (EC), [[Machine learning|Machine Learning]] (ML) and [[Probabilistic logic|Probabilistic Reasoning]] (PR), with the latter subsuming [[Bayesian network|belief networks]] and parts of learning theory.

==Introduction==
Soft Computing became a formal area of study in Computer Science in the early 1990s.<ref>Zadeh, Lotfi A., "[https://go.galegroup.com/ps/i.do?id=GALE%7CA15061349&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=00010782&p=AONE&sw=w Fuzzy Logic, Neural Networks, and Soft Computing],"  Communications of the ACM, March 1994, Vol. 37 No. 3, pages 77-84.</ref>  Earlier computational approaches could model and precisely analyze only relatively si(contracted; show full)* https://web.archive.org/web/20160310135547/http://dspace.nitrkl.ac.in:8080/dspace/bitstream/2080/1136/1/subudhi.pdf

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[[Category:Scientific modeling]]
[[Category:Artificial intelligence]]
[[Category:Semantic Web]]
[[Category:Soft computing]]