Difference between revisions 952082380 and 952086161 on enwiki

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(contracted; show full)to traditional computing, deals with approximate models and gives solutions to complex real-life problems such as [[fuzzy control system]]s, fuzzy graph theory{{sfn|Sunitha|Sunil|2013}}, [[fuzzy systems]], and so on.   Zadeh observed that people are good at 'soft' thinking while computers typically are 'hard' thinking.{{sfn|Jin|2014}}  People use concepts like 'some', 'most', or 'very' rather than 'hard' or precise concepts, values and quantities.
 In general, people are good at learning, finding 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. 

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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 |ref=harv}}

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