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Soft computing

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Concept illustration of Soft computing

Soft computing is a set of algorithms,[1] including neural networks, chaos, fuzzy logic, and evolutionary algorithms.[2]Among these, neural networks are usually used for prediction. The basic structure of multilayer perception neural network consists of the input layer, middle hidden layer and the output layer, with the product of input factors (ai) and weights (wij) fed to summing junctions with neurons bias (bj).[3] Fuzzy sets have been used to solve problems like multi-criteria decision-making, patterns recognition and diseases diagnosis.[4] Chaos has been applied chemistry and ecology research. Chaos has been studied in management operations research, such as queuing, inventory, planning and scheduling systems. These are characterized by the chaos of queue, inventory, and planning and scheduling under various management decision rules. Chaos has also been applied the management of public relations, such as problems and crises. Chaos management is dependent on change rules: a collection of principles based on orderly or disorderly changes, flexibility, and the process of new orderly emergence.[5]These algorithms are tolerant of imprecision, uncertainty, partial truth and approximation. It is contrasted with hard computing: algorithms which find provably correct and optimal solutions to problems.

History

The theory and techniques related to soft computing were first introduced in 1980s.[6] The term "soft computing" was coined by Lotfi A. Zadeh.[7][1]

See also

Notable journals

  • Soft Computing[8]
  • Applied Soft Computing[9]

References

  1. ^ a b Choudhury, Balamati; Jha, Rakesh Mohan, eds. (2016), "Soft Computing Techniques", Soft Computing in Electromagnetics: Methods and Applications, Cambridge: Cambridge University Press, pp. 9–44, doi:10.1017/CBO9781316402924.003, ISBN 978-1-107-12248-2, retrieved 2021-02-24
  2. ^ Shukla, K. K. (2000-01-01), Sinha, NARESH K.; Gupta, MADAN M. (eds.), "CHAPTER 17 - Soft Computing Paradigms for Artificial Vision", Soft Computing and Intelligent Systems, Academic Press Series in Engineering, San Diego: Academic Press, pp. 405–417, ISBN 978-0-12-646490-0, retrieved 2021-02-24
  3. ^ Sustainable Construction Safety Knowledge Sharing: A Partial Least Square-Structural Equation Modeling and A Feedforward Neural Network Approach. Sustainability 2019, 11, 5831. https://doi.org/10.3390/su11205831
  4. ^ Intuitionistic multi fuzzy ideals of near-rings. Decision Making: Applications in Management and Engineering, 20223, 6(1), 564-582. https://doi.org/10.31181/dmame04012023b
  5. ^ Dynamics Analysis and Fractional-Order Approximate Entropy of Nonlinear Inventory Management Systems, 2021, 5516703, https://www.hindawi.com/journals/mpe/2021/5516703/
  6. ^ Ibrahim, Dogan (2016-01-01). "An Overview of Soft Computing". Procedia Computer Science. 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29–30 August 2016, Vienna, Austria. 102: 34–38. doi:10.1016/j.procs.2016.09.366. ISSN 1877-0509.
  7. ^ Zadeh, Lotfi A. (1994-03-01). "Fuzzy logic, neural networks, and soft computing". Communications of the ACM. 37 (3): 77–84. doi:10.1145/175247.175255. ISSN 0001-0782. S2CID 1824401.
  8. ^ "Soft Computing". Springer. ISSN 1432-7643. Retrieved 2021-02-26.{{cite web}}: CS1 maint: url-status (link)
  9. ^ Applied Soft Computing. Elsevier B.V. ISSN 1568-4946.


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