Difference between revisions 668058160 and 680642520 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, parti(contracted; show full)asis of a considerable amount of [[machine learning]] techniques. Recent trends tend to involve evolutionary and swarm intelligence based algorithms and bio-inspired computation.<ref>X. S. Yang, Z. H. Cui, R. Xiao, A. Gandomi, M. Karamanoglu, Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, Elsevier, (2013).</ref><ref>D. K. Chaturvedi,Soft Computing: Techniques and Its Applications in Electrical Engineering, Springer, (2008).</ref>

There are main difference
s between soft computing and possibility. Possibility is used when we don't have enough information to solve a problem but soft computing is used when we don't have enough information about the problem itself. These kinds of problems originate in the human mind with all its doubts, subjectivity and emotions; an example can be determining a suitable temperature for a room to make people feel comfortable.

==Components==
Components of soft computing include:
* [[Neural network]]s (NN)
(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]]