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Soft computing

From EverybodyWiki Bios & Wiki



Not to be confused with soft microprocessor.



Soft Computing diagram

Soft computing is a branch of computer science that seeks to imitate human intelligence, learning and decision making by the employment of various methods. Soft computing employs fuzzy logic, rough sets, artificial neural networks, and various evolutionary algorithms and search strategies to further these ends.[1]

Whereas mainstream computing is referred to as "hard computing",[2] soft computing claims to bypass existing bottlenecks in science with a strong bias towards semiotics [3][4] and imprecise knowledge.[5]

History

The phrase soft computing was coined in the 1990s by Lotfi A. Zadeh, a pioneer in the field. Zadeh was also a pioneer in the related field of Fuzzy logic and the concept of fuzzy sets. According to soft computing advocacy groups, hard computing is not equipped to analyze cognitive processes.[6]

Soft computing, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems such as fuzzy control systems, fuzzy graph theory[7], fuzzy systems, and so on. Zadeh observed that people are good at 'soft' thinking while computers typically are 'hard' thinking.[8] People use concepts like 'some', 'most', or 'very' rather than 'hard' or precise concepts, values and quantities. In general, people are good at learning, finding patterns, adapting and are rather unpredictable. In 'hard' computing, by contrast, machines need precision, determinism and measures, and although pattern recognition happens, there is a brittleness if things change - such systems cannot easily adapt. 'Soft' computing by contrast embraces chaotic, neural models of computing that are more pliable. Soft computing involves using a combination of methods that are designed to approximate human learning, decision making and intelligence.

quote: "Early soft 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. "

— Kirankumar, T. M., and M. A. Jayaram. "Natural Computing in Spatial Information Systems." 2nd National Conference on Challenges & Opportunities in Information Technology (COIT-2008) RIMT-IET, Mandi Gobindgarh. March. Vol. 29. 2008.

quote: "Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve computability, robustness and low solution cost." source: [9]

As such it forms the basis of a considerable amount of machine learning techniques. Recent trends tend to involve evolutionary and swarm intelligence based algorithms and bio-inspired computation.[10][11]

quote: "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." source: [9]

quote: "hard computing schemes, which strive for exactness and formal proof, 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." source: [9]

Research in soft computing have demonstrated some practical examples,[12][13] such as Neuron MOS transistors [14] and emotional pets.[15]

Within the history of Artificial Intelligence there is a trend towards including linguistics and probability theory into existing software frameworks.[16] The highly successful innovation of deep learning is based on the idea, that input data is transformed into abstract representation.

Beyond Turing machines

BQP complexity class diagram

In classical so called hard computing there is a bottleneck available[17] which has to do how a turing machine is working. For solving a task, a turing machine needs a certain amount of steps, which are executed by the algorithm. If an algorithm needs to much steps to solve a problem, it's called an np hard problem, which means, that every turing machine fails to solve this sort of problem.

Soft computing claims to bypass the Turing limit by introducing a different kind of computational paradigm, which is called a super-turing machine.[18][19] Super Turing machines are able to solve all the np hard problems by grounding the algorithm to the outside world.[20] Grounding means to connect the numerical values in a fuzzy set with natural language terms which are describing the problem.[21] The discipline of granular computing[22] is equal to soft computing and can be interpreted as the opposite to classical hard computing.

Definition of the term

Overview articles and surveys are created to analyze a subject from a broader perspective. Many overview articles about soft computing are available. They are trying to define the term and give a historic timeline. The first important thing to know is, that it is complicated to define soft computing, because the definition has evolved from 1990 to 2020 over many steps.[23]

Most authors agree that soft computing describes a family of techniques. This is equal to a hybrid system. It includes elements of imprecision, learning and evolutionary computation.[24] A term which describes the same situation is Computational Intelligence.[25]

References

  1. ↑ Samir & Chakraborty 2013.
  2. ↑ Seising 2011, pp. 3-36.
  3. ↑ Gudwin 1999.
  4. ↑ Rieger 1998.
  5. ↑ Ibrahim 2016, pp. 34-38.
  6. ↑ Gudwin 1999, p. 160.
  7. ↑ Sunitha & Sunil 2013.
  8. ↑ Jin 2014.
  9. ↑ 9.0 9.1 9.2 Gupta, Puja and Kulkarni, Neha (2013). "An Introduction of Soft Computing Approach over Hard Computing". International Journal of Latest Trends in Engineering and Technology (IJLTET). 3 (1): 254--258.CS1 maint: Multiple names: authors list (link)
  10. ↑ Yang 2013.
  11. ↑ Chaturvedi 2008.
  12. ↑ Zhou 2000, pp. 238-250.
  13. ↑ Karray 2002.
  14. ↑ Shibata 1999, pp. 648-656.
  15. ↑ Dote 2001.
  16. ↑ Seising 2013.
  17. ↑ B. Sick and M. Keidl and M. Ramsauer and S. Seltzsam (1998). A Comparison of Traditional and Soft-Computing Methods in a Real-Time Control Application. ICANN 98. Springer London. pp. 725--730. doi:10.1007/978-1-4471-1599-1_111.
  18. ↑ Yongming Li (2006). Some Results of Fuzzy Turing Machines. 2006 6th World Congress on Intelligent Control and Automation. IEEE. doi:10.1109/wcica.2006.1713000.
  19. ↑ Cristian S. Calude and Gheorghe P\uaun (2004). "Bio-steps beyond Turing". Biosystems. Elsevier BV. 77 (1–3): 175--194. doi:10.1016/j.biosystems.2004.05.032.
  20. ↑ Silvia Coradeschi and Alessandro Saffiotti (2003). "An introduction to the anchoring problem". Robotics and Autonomous Systems. Elsevier BV. 43 (2–3): 85--96. doi:10.1016/s0921-8890(03)00021-6.
  21. ↑ Wang, Yingxu (2010). "On concept algebra for computing with words (CWW)". International Journal of Semantic Computing. World Scientific. 4 (03): 331--356. doi:10.1142/s1793351x10001061.
  22. ↑ Bargiela, Andrzej and Pedrycz, Witold (2006). The roots of granular computing. 2006 IEEE International Conference on Granular Computing. IEEE. pp. 806--809.CS1 maint: Multiple names: authors list (link)
  23. ↑ Piero P. Bonissone (2010). "Soft Computing: A Continuously Evolving Concept". International Journal of Computational Intelligence Systems. Atlantis Press. 3 (2): 237--248. doi:10.1080/18756891.2010.9727694.
  24. ↑ Enric Trillas and Claudio Moraga and Sergio Guadarrama (2010). "A (naive) glance at Soft Computing". International Journal of Computational Intelligence Systems. Atlantis Press. 3 (2): 197. doi:10.2991/ijcis.2010.3.2.7.
  25. ↑ L. Magdalena (2010). "What is Soft Computing? Revisiting Possible Answers". International Journal of Computational Intelligence Systems. Atlantis Press. 3 (2): 148--159. doi:10.1080/18756891.2010.9727686.

Bibliography

  • Samir, Rov; Chakraborty, Udit (3 June 2013). Introduction to soft computing : neuro-fuzzy and genetic algorithms. Pearson. ISBN 978-8131792469. Search this book on
  • Seising, Rudolf; Sanz, Veronica (2011). "From Hard Science and Computing to Soft Science and Computing – an Introductory Survey". From Hard Science and Computing to Soft Science and Computing An Introductory Survey. Studies in Fuzziness and Soft Computing. 273. Springer Berlin Heidelberg. doi:10.1007/978-3-642-24672-2_1. ISBN 978-3-642-24671-5. Search this book on
  • Gudwin, Ricardo R (1999). From semiotics to computational semiotics. Proceedings of the 9th International Congress of the German Society for Semiotic Studies, 7th International Congress of the International Association for Semiotic Studies (IASS/AIS).
  • Rieger, B (1998). Computing Fuzzy Semantic Granules from Natural Language Texts. Proc. 7th IPMU Conf. pp. 475–479.
  • Ibrahim, Dogan (2016). "An Overview of Soft Computing". Procedia Computer Science. Elsevier BV. 102: 34–38. doi:10.1016/j.procs.2016.09.366.
  • Seising, Rudolf (2010). "What is Soft Computing? Bridging Gaps for 21st Century Science!". International Journal of Computational Intelligence Systems. Atlantis Press. 3 (2). doi:10.2991/ijcis.2010.3.2.4.
  • Zhou, C (2000). "Neuro-fuzzy gait synthesis with reinforcement learning for a biped walking robot". Soft Computing. Springer Science and Business Media LLC. 4 (4): 238–250. doi:10.1007/s005000000053.
  • Karray, F; Gueaieb, W; Al-Sharhan, S (2002). "The hierarchical expert tuning of PID controllers using tools of soft computing". IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). Institute of Electrical and Electronics Engineers (IEEE). 32 (1): 77–90. doi:10.1109/3477.979962. PMID 18238106.
  • Shibata, Tadashi; Yagi, Masakazu; Adachi, Masayoshi (1999). Soft-computing integrated circuits for intelligent information processing. Proceedings of The Second International Conference on Information Fusion. 1. pp. 648–656.
  • Dote, Y; Ovaska, S.J. (2001). "Industrial applications of soft computing: a review". Proceedings of the IEEE. Institute of Electrical and Electronics Engineers (IEEE). 89 (9): 1243–1265. doi:10.1109/5.949483.
  • Seising, Rudolf; Marco Elio, Tabacchi (2013). A very brief history of soft computing: Fuzzy Sets, artificial Neural Networks and Evolutionary Computation. 2013 Joint IFSA World Congress and NAFIPS Annual. IEEE. doi:10.1109/ifsa-nafips.2013.6608492.
  • Yang, X. S.; Cui, Z. H.; Xiao, R.; Gandomi, A.; Karamanoglu, M. (2013). Swarm Intelligence and Bio-Inspired Computation: Theory and Applications. Elsevier. Search this book on
  • Chaturvedi, D. K. (2008). Soft Computing: Techniques and Its Applications in Electrical Engineering. Springer. Search this book on
  • Sunitha, M.S.; Sunil, Matthew (2013). "Fuzzy Graph Theory: A Survey" (PDF).
  • Jin, Yaochu (2014). "Soft Computing Home Page - Short Definition of Soft Computing".
  • Zadeh, Lotfi A. (March 1994). "Fuzzy Logic, Neural Networks, and Soft Computing". Communications of the ACM. 37 (3): 77–84.


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



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