Difference between revisions 943727025 and 943866715 on enwiki

{{Distinguish|soft microprocessor}}

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{{Expert needed|computer science|date=July 2009}}
{{Notability|date=August 2019}}
{{Essay-like|date=August 2019}}
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Unlike hard computing schemes, which strive for exactness and full truth, 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.

==
Overview==
* opposition is "hard computing"
* is part of Computational Semiotics [6]
* is related to cybernetics 
* “Hard science” is dominated by mathematical precision [9], “hard science” has to do with psychology and humanities
* exact models are numerical, approximate models are imprecise [10]
* is a general system theory [5] about anything and nothing
* Cognitive processes are analyzed with Computational Semiotics [8] 

==Keypoints==
[[File:Soft Computing pic.jpg|thumb|Soft Computing pic]]
* fuzzy logic & soft computing
* soft computing: fuzzy logic, neural networks, genetic algorithm and probabilistic robotics. 
* Soft computing = Computational Intelligence
* term was introduced in the 1990s [1], Term was introduced in 1991 by Lotfi A Zadeh
* application: emotional pet [2], biped walking [3], self-tuning controller [4]
* can be implemented in hardware, for example with the Neuron MOS Transistor [7]
* can be reduced to neural networks -> no it's wrong because neural network are numerical models
* machine learning is oriented on statistics, while soft computing is focussed on linugistics and fuzzy logic
* "soft computing" vs "artificial intelligence": shared similarity are neural networksProse text==
[[File:Soft Computing pic.jpg|thumb|Soft Computing pic]]
'''Soft computing''' is an insignificant discipline within Artificial Intelligence which is trying to redefine existing computer science. The mainstream attempt of creating intelligent machines is described with a pejoratively connotation as “hard computing”.[9] Soft computing claims to bypass existing bottlenecks in science with a strong focus on [[semiotics]] [6][8] and imprecise knowledge.[10] At the same time, it's implied that the normal, so called “hard science” isn't motivated to deal with [[probability]] and with the [[symbol grounding problem]].

In the early 1990s the first papers about Soft computing were published by [[Lotfi Zadeh]], which is known as the father of [[Fuzzy logic]]. He improved an esoteric range-based logic system into a [[universal science]] which stands in opposition to Artificial Intelligence. Soft computing isn't a positive independent theory about a new sort of neural networks or a certain algorithm to process information but it's a critique on existing artificial Intelligence research. According to soft computing [[advocacy group]]s, normal computer science isn't able to analyze cognitive processes [5] and it's using the wrong sort of [[mathematical statistics]].

There are some research projects ongoing in which the theory of soft computing was demonstrated for practical examples.[3][4] Dedicated [[MOS transistor]]s where build [7] and emotional pets [2] were designed. Most of these make-shift projects are ignored by machine learning experts because the existing mathematical tools for building CPUs and intelligent robots are sufficient to fulfill current and future demands.

Within the history of Artificial Intelligence there is a trend available to include [[linguistics]] and probability theory into existing software frameworks.[1] The highly successful innovation of [[deep learning]] is based on the idea, that input data is transformed into abstract representation. This development shows, that so called hard science is well equipped to deal with imprecise information and there is no need to introduce fuzzy logic.

; literature
* [1] Seising, Rudolf, and Marco Elio Tabacchi. "A very brief history of soft computing: Fuzzy Sets, artificial Neural Networks and Evolutionary Computation." 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS). IEEE, 2013.
* [2] Dote, Yasuhiko, and Seppo J. Ovaska. "Industrial applications of soft computing: a review." Proceedings of the IEEE 89.9 (2001): 1243-1265.
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* [8] Rieger, B. "Computing Fuzzy Semantic Granules from Natural Language Texts." Proc. 7th IPMU Conf.. 1998.
* [9] Seising, Rudolf, and Veronica Sanz. "From hard science and computing to soft science and computing–an introductory survey." Soft computing in humanities and social sciences. Springer, Berlin, Heidelberg, 2012. 3-36.
* [10] Ibrahim, Dogan. "An overview of soft computing." Procedia Computer Science 102 (2016): 34-38.


==Some pro arguments==
* remaining sections written in natural language can be aggregated into a single section
* here comes the existing text⏎
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===Remove from text===
* section components: bullet points with links to other articles doesn't contains valuable information
* section external links: URL to external websites are not referenced in the text -> can be deleted
* remaining sections written in natural language can be aggregated into a single section⏎
==Keypoints==
===Overview===
* opposition is "hard computing"
* is part of Computational Semiotics [6]
* is related to cybernetics 
* “Hard science” is dominated by mathematical precision [9], “hard science” has to do with psychology and humanities
* exact models are numerical, approximate models are imprecise [10]
* is a general system theory [5] about anything and nothing
* Cognitive processes are analyzed with Computational Semiotics [8] 

===details===
* fuzzy logic & soft computing
* soft computing: fuzzy logic, neural networks, genetic algorithm and probabilistic robotics. 
* Soft computing = Computational Intelligence
* term was introduced in the 1990s [1], Term was introduced in 1991 by Lotfi A Zadeh
* application: emotional pet [2], biped walking [3], self-tuning controller [4]
* can be implemented in hardware, for example with the Neuron MOS Transistor [7]
* can be reduced to neural networks -> no it's wrong because neural network are numerical models
* machine learning is oriented on statistics, while soft computing is focused on linguistics and fuzzy logic
* "soft computing" vs "artificial intelligence": shared similarity are neural networks

==References==
{{reflist}}

==External links==
* [http://www.softcomputing.es EUROPEAN CENTRE FOR SOFT COMPUTING]
* [https://web.archive.org/web/20080106133957/http://www.helsinki.fi/~niskanen/bisc.html BISC SIG IN PHILOSOPHY OF SOFT COMPUTING]
* [https://www.cs.upc.edu/~websoco/ SOCO: UPC Group on Soft Computing Systems]
* http://www.soft-computing.de/def.html
* https://web.archive.org/web/20160310135547/http://dspace.nitrkl.ac.in:8080/dspace/bitstream/2080/1136/1/subudhi.pdf

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