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{{Distinguish|soft microprocessor}}

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(contracted; show full)ng 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 simple systems. More complex systems arising in [[biology]], [[medicine]], the [[humanities]], [[management science]]s, and similar fields often remained intractable to conventional mathematical and analytical methods.  However, it should be pointed out that complexity of systems is relative and that many conventional mathematical models have been very productive in spite of their complexity.

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.<ref>X. S. Yang, Z. H. Cui, R. Xiao, A. Gandomi, M. Karamanoglu, [https://books.google.com/books?id=J0VcBQxtcwsC&printsec=frontcover#v=onepage&q=%22soft%20computing%22&f=false Swarm Intelligence and Bio-Inspired Computation: Theory and Applications], Elsevier, (2013).</ref><ref>D. K. Chaturvedi, "[https://books.google.com/books?id=Igw6WDcfmp4C&printsec=frontcover#v=onepage&q&f=false Soft Computing: Techniques and Its Applications in Electrical Engineering]", Springer, (2008).</ref>

==Components==
Components of soft computing include:
*[[Machine learning]], including:
** [[Neural network]]s (NN)
*** [[Perceptron]]
** [[Support Vector Machine]]s (SVM)
(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]]