Difference between revisions 728131201 and 734161353 on enwiki

{{Distinguish|soft microprocessor}}
{{Expert subject|computer science|date=July 2009}}

In [[computer science]], '''soft computing''' (sometimes referred to as [[computational intelligence]], though CI does not have an agreed definition) is the use of inexact solutions to computationally hard tasks such as the solution of [[NP-complete]] problems, for which there is no known algorithm that can compute an exact solution in [[polynomial time]]. Soft computing differs from convent(contracted; show full)* [[Evolutionary computation]] (EC), including:
** [[Evolutionary algorithm]]s
*** [[Genetic algorithm]]s
*** [[Differential evolution]]
** [[Metaheuristic]] and [[Swarm Intelligence]]
*** [[Ant colony optimization]]
*** [[Particle swarm optimization]]

*** [[Firefly algorithm]]
*** [[Cuckoo search]]
*** [[Flower pollination algorithm]]⏎
* Ideas about [[probability]] including:
** [[Bayesian network]]
* [[Chaos theory]]

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.

==Applications==

===Bioinformatics and Biomedicine===

SC has attracted close attention of researchers and has also been applied successfully to solve problems in bioinformatics and biomedicine . Nevertheless, the amount of information from biological experiments and the applications involving large-scale high-throughput technologies is rapidly increasing nowadays. Therefore, the ability of being scalable across large-scale problems becomes an essential requirement for modern SC approaches.<ref>{{cite journal|last1=Yudong|first1=Zhang|last2=Saeed|first2=Balochian|last3=Vishal|first3=Bhatnagar|title=Emerging Trends in Soft Computing Models in Bioinformatics and Biomedicine|journal=The Scientific World Journal|date=2014|volume=2014|pages=3|doi=10.1155/2014/683029|url=http://www.hindawi.com/journals/tswj/si/209027/}}</ref>

=== Control of a flexible robot manipulator===

The conventional approach to design of controllers for any plant, process or system
requires knowledge of an accurate mathematical model of the system to be controlled,
which is often difficult to derive analytically. In consequence, it is difficult or impossible
to design controllers for complex systems such as nonlinear multivariable systems using these conventional approaches that require a plant model.

A number of research investigations
exploit soft computing approaches such as fuzzy logic and neural network techniques in
designing improved controllers for flexible link manipulators. 
Controlling the tip position of a single-link flexible manipulator has been achieved successfully by
employing neural-network and fuzzy controllers

An intelligent optimal control for a nonlinear flexible robot arm
driven by a permanent-magnet synchronous servo motor has been designed using a fuzzy
neural network control approach.⏎
⏎
==References==
{{reflist}}

==External links==
* [http://www.softcomputing.es EUROPEAN CENTRE FOR SOFT COMPUTING]
* [http://www.helsinki.fi/~niskanen/bisc.html BISC SIG IN PHILOSOPHY OF SOFT COMPUTING]
* http://www.soft-computing.de/def.html
* http://dspace.nitrkl.ac.in:8080/dspace/bitstream/2080/1136/1/subudhi.pdf

{{Authority control}}

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