Soft computing 201158 225089017 2008-07-11T20:43:06Z Andreas Kaufmann 72502 Removed category "Computer science" (using [[WP:HOTCAT|HotCat]]) '''Soft computing''' refers to a collection of computational techniques in [[computer science]], [[machine learning]] and some engineering disciplines, which study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Soft Computing uses soft techniques contrasting it with classical [[artificial intelligence]] hard computing techniques. [[Hard computing]] is bound by a Computer Science concept called [[NP-Complete]], which means, in layman's terms, that there is a direct connection between the size of a problem and the amount of resources needed to solve the problem (there are problems so large that it would take the lifetime of the [[Universe]] to solve them, even at [[super computing]] speeds). Soft computing aids to surmount NP-complete problems by using inexact methods to give useful, but inexact, answers to [[intractable]] problems. == Introduction == Soft Computing became a formal Computer Science area of study in the early 1990's.<ref>Sadeh, Lotfi A., "Fuzzy Logic, Neural Networks, and Soft Computing," Communications of the ACM, March 1994, Vol. 37 No. 3, pages 77-84.</ref> 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. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Components of soft computing include: * [[Neural network]]s (NN) * [[Fuzzy system]]s (FS) * [[Evolutionary computation]] (EC), including: ** [[Evolutionary algorithm]]s ** [[Harmony search]] * [[Swarm intelligence]] * 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. ==See also== * [[Estimation of distribution algorithm]] * [[Soft science]] * [[Rough set|Rough set theory]] * [[Genetic algorithm]] * [[Neural networks]] * [[Fuzzy logic]] * [[Support vector machines]] == References == <references/> == Bibliography == Ajith Abraham, Nature and Scope of AI Techniques, Handbook for Measurement Systems Design, Peter Sydenham and Richard Thorn (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, pp. 893-900, 2005. Ajith Abraham, Artificial Neural Networks, Handbook for Measurement Systems Design, Peter Sydenham and Richard Thorn (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, pp. 901-908, 2005. Ajith Abraham, Rule Based Expert Systems, Handbook for Measurement Systems Design, Peter Sydenham and Richard Thorn (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, pp. 909-919, 2005. Ajith Abraham, Evolutionary Computation, Handbook for Measurement Systems Design, Peter Sydenham and Richard Thorn (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, pp. 920-931, 2005. Ajith Abraham, Adaptation of Fuzzy Inference System Using Neural Learning, Fuzzy System Engineering: Theory and Practice, Nadia Nedjah et al. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, ISBN 3-540-25322-X, Chapter 3, pp. 53-83, 2005. Ajith Abraham and Crina Grosan, Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews, Engineering Evolutionary Intelligent Systems, Studies in Computational Intelligence, Springer Verlag, Germany, ISBN 978-3-540-75395-7, pp. 1-22, 2008. Ajith Abraham, Swagatam Das and Sandip Roy, Swarm Intelligence Algorithms for Data Clustering, Soft Computing for Knowledge Discovery and Data Mining, Oded Maimon and Lior Rokach (Eds.), Springer Verlag, Germany, ISBN 978-0-387-69934-9, pp. 279-313, 2007. ==External links== * [http://www.helsinki.fi/~niskanen/bisc.html BISC SIG IN PHILOSOPHY OF SOFT COMPUTING] [[Category:Scientific modeling]] [[Category:Artificial intelligence]] [[Category:Semantic Web]] [[fa:محاسبات نرم]] [[it:Soft computing]] [[hu:Lágy számítási modell]] [[nl:Soft computing]] [[ja:ソフトコンピューティング]] [[zh:软计算]]