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Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve computability, robustness and low solution cost. As such it forms the basis of a considerable amount of [[machine learning]] techniques. Recent trends tend to involve evolutionary and swarm intelligence based algorithms and [[bio-inspired computation]].{{sfn|Yang|2013}}{{sfn|Chaturvedi|2008}}


{{Quote
|text=quote: "Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal [[logical system]]s, such as [[sentential logic]] and [[predicate logic]], or rely heavily on computer-aided numerical analysis (as in [[finite element analysis]]). Soft computing techniques are intended to complement each other."
| source=Gupta, Puja, and Neha Kulkarni. "An Introduction of Soft Computing Approach over Hard Computing." International Journal of Latest Trends in Engineering and Technology (IJLTET) 3.1 (2013): 254-258.
}}

Unlike hard computing schemes, which strive for exactness and formal proof, soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. Another common contrast comes from the observation that [[inductive reasoning]] plays a larger role in soft computing than in hard computing.

(contracted; show full)unding the algorithm to the outside world.<ref>{{cite journal |doi=10.1016/s0921-8890(03)00021-6 |year=2003 |publisher=Elsevier BV |volume=43 |number=2-3 |pages=85--96 |author=Silvia Coradeschi and Alessandro Saffiotti |title=An introduction to the anchoring problem |journal=Robotics and Autonomous Systems }}</ref> Grounding means to connect the numerical values in a fuzzy set with natural language terms which are describing the problem.<ref>{{cite journal |doi=10.1142/s1793351x10001061 |
year=2010 |publisher=World Scientific Pub Co Pte Lttitle=On concept algebra for computing with words (CWW) |author=Wang, Yingxu |journal=International Journal of Semantic Computing |volume=04 |number=03 |pages=331--356 |author=YINGXU WANG |title=ON CONCEPT ALGEBRA FOR COMPUTING WITH WORDS (CWW) |journal=International Journal of Semantic Computingyear=2010 |publisher=World Scientific }}</ref> The discipline of granular computing<ref>{{cite conference |title=The roots of granular computing |author=Bargiela, Andrzej and Pedrycz, Witold |conference=2006 IEEE International Conference on Granular Computing |pages=806--809 |year=2006 |publisher=IEEE }}</ref> is equal to soft computing and can be interpreted as the opposite to classical hard computing.

==References==
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==Bibliography==
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* {{cite journal |last1=Zadeh |first1=Lotfi A. |url=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 |title=Fuzzy Logic, Neural Networks, and Soft Computing| journal= Communications of the ACM|date=March 1994 |volume=37 |number=3| pages =77-84 }}

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