Revision 944863858 of "Draft:Soft computing" on enwiki

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{{AFC comment|1=This should be discussed whether this needs to be a separate article from computational intelligence. [[User:AngusWOOF|<strong><span style="color: #606060;">AngusWOOF</span></strong>]] ([[User talk:AngusWOOF#top|<span style=" color: #663300;">bark</span>]] • [[Special:Contributions/AngusWOOF|<span style="color: #006600;">sniff</span>]]) 16:04, 9 March 2020 (UTC)}}

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{{Distinguish|soft microprocessor}}

{{Draft}}

{{Multiple issues|
{{Expert needed|computer science|date=July 2009}}
{{Notability|date=August 2019}}
{{Essay-like|date=August 2019}}
{{Confusing|date=August 2019}}
{{Incomprehensible|date=August 2019}}
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[[File:Soft Computing pic.jpg|thumb|Soft Computing diagram]]
'''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”.{{sfn|Seising|2011|pp=3-36}} Soft computing claims to bypass existing bottlenecks in science with a strong bias towards [[semiotics]] {{sfn|Gudwin|1999}}{{sfn|Rieger|1998}} and imprecise knowledge.{{sfn|Ibrahim|2016|pp=34-38}} 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]].

==Criticism of computer science==
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 a 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 teaching. According to soft computing [[advocacy group]]s, normal computer science isn't able to analyze cognitive processes {{sfn|Gudwin|1999|p=160}} and its 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.{{sfn|Zhou|2000|pp=238-250}}{{sfn|Karray|2002}} Neuron [[MOS transistor]]s where build {{sfn|Shibata|1999|pp=648-656}} and emotional pets {{sfn|Dote|2001}} 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.{{sfn|Seising |2013}} 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 has the right tools to deal with imprecise information and there is no need to introduce fuzzy logic.

==Thinking with approximate models==
Soft computing, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems.{{clarify|date=January 2020}} It was conceived by [[Lotfi Zadeh]], pioneer of a mathematical concept known as [[fuzzy sets]] which led to many new fields 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 of 3.5 or 102. People want a 'warm' glass of milk, not one that is 102 degrees. 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 - it cannot easily adapt. 'Soft' computing by contrast embraces chaotic, neural models of computing that are more pliable.  Because there is no known single method that lets us compute like people,  soft computing involves using a combination of methods that each bring something helpful to achieve this goal.

Soft Computing became a formal area of study in Computer Science in the early 1990s.{{sfn|Zadeh|1994|pp=77-78}}{{failedverification|date=January 2020}}  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.  Complexity of systems is relative and many conventional mathematical models have been very productive in spite of their complexity.{{cn|date=January 2020}}

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]].{{sfn|Yang|2013}}{{sfn|Chaturvedi|2008}}

Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal [[logical system]]s, 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.

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.

==References==
{{reflist}}

==Bibliography==
*{{cite book |last1=Samir |first1=Rov |last2=Chakraborty |first2=Udit |title=Introduction to soft computing : neuro-fuzzy and genetic algorithms |date=3 Jun. 2013 |publisher=Pearson |isbn=978-8131792469 |ref=harv }}
* {{cite book |doi=10.1007/978-3-642-24672-2_1 |isbn=978-3-642-24671-5 |date=2011 |publisher=Springer Berlin Heidelberg |first1=Rudolf |last1=Seising |first2=Veronica | last2=Sanz  |title=From Hard Science and Computing to Soft Science and Computing An Introductory Survey |chapter=From Hard Science and Computing to Soft Science and Computing – an Introductory Survey |volume=273 |conference=Soft Computing in Humanities and Social Sciences |series=Studies in Fuzziness and Soft Computing |ref=harv}}
* {{cite conference |title=From semiotics to computational semiotics |last1=Gudwin |first1=Ricardo R |conference=Proceedings of the 9th International Congress of the German Society for Semiotic Studies, 7th International Congress of the International Association for Semiotic Studies (IASS/AIS) |date=1999 | ref=harv }}
* {{cite conference |title=Computing Fuzzy Semantic Granules from Natural Language Texts |last1=Rieger|first1=B |conference=Proc. 7th IPMU Conf. |pages=475–479 |date=1998 |ref=harv }}
* {{cite journal |doi=10.1016/j.procs.2016.09.366 |date=2016 |publisher=Elsevier BV |volume=102 |pages=34–38 |last1=Ibrahim |first1=Dogan  |title=An Overview of Soft Computing |journal=Procedia Computer Science |ref=harv }}
* {{cite journal |doi=10.2991/ijcis.2010.3.2.4 |date=2010 |publisher=Atlantis Press |volume=3 |number=2 |first1=Rudolf |last1=Seising  |title=What is Soft Computing? Bridging Gaps for 21st Century Science! |journal=International Journal of Computational Intelligence Systems| ref=harv }}
* {{cite journal |doi=10.1007/s005000000053 |date=2000 |publisher=Springer Science and Business Media LLC |volume=4 |number=4 |pages=238–250 |last1=Zhou |first1=C |title=Neuro-fuzzy gait synthesis with reinforcement learning for a biped walking robot |journal=Soft Computing| ref=harv  }}
* {{cite journal |doi=10.1109/3477.979962 |pmid=18238106 |date=2002 |publisher=Institute of Electrical and Electronics Engineers (IEEE) |volume=32 |number=1 |pages=77–90 |last1=Karray|first1=F |last2=Gueaieb|first2=W |last3=Al-Sharhan|first3=S |title=The hierarchical expert tuning of PID controllers using tools of soft computing |journal=IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)| ref=harv  }}
* {{cite conference |title=Soft-computing integrated circuits for intelligent information processing |last1=Shibata|first1=Tadashi|last2=Yagi|first2=Masakazu|last3=Adachi|first3=Masayoshi |conference=Proceedings of The Second International Conference on Information Fusion |volume=1 |pages=648–656 |date=1999| ref=harv  }}
* {{cite journal |doi=10.1109/5.949483 |date=2001 |publisher=Institute of Electrical and Electronics Engineers (IEEE) |volume=89 |number=9 |pages=1243–1265 |last1= Dote|first1=Y|last2=Ovaska|first2=S.J.  |title=Industrial applications of soft computing: a review |journal=Proceedings of the IEEE| ref=harv  }}
* {{cite conference |doi=10.1109/ifsa-nafips.2013.6608492 |date=2013 |publisher=IEEE |first1=Rudolf |last1=Seising |first2=Tabacchi|last2=Marco Elio  |title=A very brief history of soft computing: Fuzzy Sets, artificial Neural Networks and Evolutionary Computation |conference=2013 Joint IFSA World Congress and NAFIPS Annual 
* {{cite book |first1=X. S. |last1=Yang|first2=Z. H. |last2=Cui |first3=R. |last3=Xiao |first4=A. |last4=Gandomi |first5=M. |last5=Karamanoglu | url=https://books.google.com/books?id=J0VcBQxtcwsC&printsec=frontcover#v=onepage&q=%22soft%20computing%22&f=false | title=Swarm Intelligence and Bio-Inspired Computation: Theory and Applications | publisher=Elsevier |date=2013 |ref=harv}}
* {{cite book |first1=D. K. |last1=Chaturvedi |url=https://books.google.com/books?id=Igw6WDcfmp4C&printsec=frontcover#v=onepage&q&f=false |title=Soft Computing: Techniques and Its Applications in Electrical Engineering |publisher=Springer |date=2008 |ref=harv}}
* {{cite web | url=http://researchmathsci.org/apamart/apam-v4n1-3.pdf |date=2013 |title=Fuzzy Graph Theory: A Survey |last1=Sunitha|first1=M.S.|last2=Sunil |first2=Matthew |ref=harv}}
* {{Cite web | url=http://soft-computing.de/def.html | last1=Jin |first1=Yaochu | date=2014| title=Soft Computing Home Page - Short Definition of Soft Computing |ref=harv}}
* {{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]]