Soft computing
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Soft computing refers to a collection of computational techniques in computer science, artificial intelligence, machine learning and some engineering disciplines, which attempt to study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. 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.
Key areas of soft computing include:
- Neural networks (NN)
- Fuzzy systems (FS)
- Evolutionary computation, including: (EC)
- 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 often 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
- Philosophy of soft computing
- Estimation of distribution algorithm
- Soft science
- Rough set theory
- Genetic algorithm
- Neural networks
- Fuzzy logic
- Support vector machines
[edit] References
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.
