Soft computing
From Wikipedia, the free encyclopedia
 |
This article is in need of attention from an expert on the subject. WikiProject Computer science or the Computer science Portal may be able to help recruit one. |
Soft computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally-hard tasks such as the solution of NP-complete problems, for which an exact solution cannot be derived in polynomial time.
Contents |
[edit] Introduction
Soft Computing became a formal Computer Science area of study in the early 1990's.[1] Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, 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 networks (NN)
- Fuzzy systems (FS)
- Evolutionary computation (EC), including:
- Swarm intelligence
- Ideas about probability including:
- Chaos theory
Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, 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.
[edit] See also
- Estimation of distribution algorithm
- Soft science
- Rough set theory
- Genetic algorithm
- Neural networks
- Fuzzy logic
- Support vector machines
- Type-2 fuzzy sets and systems
[edit] References
- ^ Zadeh, Lotfi A., "Fuzzy Logic, Neural Networks, and Soft Computing," Communications of the ACM, March 1994, Vol. 37 No. 3, pages 77-84.
[edit] Bibliography
Abraham,A., 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.
Abraham,A., 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.
Abraham,A., 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.
Abraham,A., 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.
Abraham,A., 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.
Abraham,A., and Grosan, C., 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.
Abraham,A., Das, S., and Roy, S., 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.
