Difference between revisions 966786223 and 966790806 on enwiki

{{AFC submission|d|mergeto|Computational intelligence|u=ManuelRodriguez|ns=118|decliner=AngusWOOF|declinets=20200309160440|ts=20200309093942}} <!-- Do not remove this line! -->

(contracted; show full)patterns, adapting and are rather unpredictable. In 'hard' computing, by contrast, machines need precision, determinism and  measures, and although pattern recognition happens, there is a brittleness if things change - such systems cannot easily adapt. 'Soft' computing by contrast embraces chaotic, neural models of computing that are more pliable.  Soft computing involves using a combination of methods that are designed to approximate human learning, decision making and intelligence. 


{{Quote
|text=quote: "Early soft 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.⏎
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Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve computability, robustness and low solution cost.  "
| source=Kirankumar, T. M., and M. A. Jayaram. "Natural Computing in Spatial Information Systems." 2nd National Conference on Challenges & Opportunities in Information Technology (COIT-2008) RIMT-IET, Mandi Gobindgarh. March. Vol. 29. 2008.
}} 

{{Quote
|text=quote: "Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve computability, robustness and low solution cost."
| 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.
}} ⏎
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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 systems, 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 {{Quote
|text=quote: "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." 
| 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.
}}

Research in soft computing have demonstrated some practical examples,{{sfn|Zhou|2000|pp=238-250}}{{sfn|Karray|2002}} such as Neuron [[MOS transistor]]s {{sfn|Shibata|1999|pp=648-656}} and emotional pets.{{sfn|Dote|2001}}

(contracted; show full)
* {{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]]