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- 1. Research Scholar Introduction to Soft Computing TAMEEM AHMAD PRESENTED BY: Department of Computer Engineering, ZHCET Zakir husain College of Engineering & Technology
- 2. About AMU • Ranked 5th spot on India Today-Neilsen Universities Ranking 2012-13. • Indian educational institutions by the UK based Times India Reputation Rankings of 2012-13 published in the Times Higher Education, London, AMU ranks as the 9th best Indian educational institution. • Ranked 50 among top 100 institutions of higher learning in BRICS (the group of newly developed and industrialized countries including Brazil, Russia, India, China and South Africa), while JNU holds 57th position. About ZHCET • Identified out of 400 institutions across the country and put in the list of 7 selected institution to be upgraded to the level of IIT. • Got 10crore under TEQUIP. • MIT open courseware centre More than 1200 acres campus 30,000 Students 2,000 Academic Staff 12 Faculties 109 Departments 5 Institutions 13 Centers 19 Halls of Residence 73 Hostels 23 CountriesStudents
- 3. Soft Computing • Research and Scope
- 4. Soft Computing, What is it? • The idea behind soft computing is to model cognitive behavior of human mind. • Soft computing is foundation of conceptual intelligence in machines. • Use inexact solution to computationally hard tasks (such as solution for NP-Complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time) • Unlike hard computing , Soft computing is tolerant of imprecision, uncertainty, partial truth, and approximation.
- 5. Soft Computing, What is it? (Cont…) • The idea of softcomputing was initiated in 1981 when Lofti A.Zadeh published his first paper on soft data analysis “what is softcomputing”, softcomputing. Springer-Verlag Germany/ USA, 1997. • Zedeh, define softcomputing into one multidisciplinary system as the fusion of the fields of Fuzzy Logic, Neuro-computing, Evolutionary computing and Probabilistic Computing. • Lofti A. Zedah, 1992: “softcomputing is an emerging approach to computing which parallel the remarkable ability of human mind to reason and learn in the environment of uncertainly and imprecision”
- 6. Soft Computing, What is it? (Cont…) • Unlike hard computing , Soft computing is tolerant of – imprecision, – uncertainty, – partial truth, and – approximation
- 7. Hard Vs Soft Computing ∙ Hard computing − Based on the concept of precise modelling (mathematical or analytical) and analyzing to yield accurate results. − Works well for simple problems, but is bound by the NP- Complete set. ∙ Soft computing − Aims to surmount NP-complete problems. − Uses inexact methods to give useful but inexact answers to intractable problems. − Represents a significant paradigm shift in the aims of computing - a shift which reflects the human mind. − Tolerant to imprecision, uncertainty, partial truth, and approximation. − Well suited for real world problems where ideal models are not available.
- 8. Hard Vs Soft Computing (Cont…) Hard Computing Soft Computing Conventional computing requires a precisely stated analytical model. Soft computing is tolerant of imprecision. Often requires a lot of computation time. Can solve some real world problems in reasonably less time. Not suited for real world problems for which ideal model is not present. Suitable for real world problems. It requires full truth Can work with partial truth It is precise and accurate Imprecise. High cost for solution Low cost for solution Require programs to be written Can evolve its own programs Deterministic Stochastic Require exact input Can deal with ambiguous and noisy data Produce precise answer Produce approximate answers
- 9. Possibility Vs Soft Computing Possibility Soft Computing Does not have enough information to solve the problem Does not have enough information about the problem itself.
- 10. Constituents of Soft Computing − Fuzzy Logic (FL) − Evolutionary Computation (EC) - based on the origin of the species Genetic Algorithm Swarm Intelligence Ant Colony Optimizations − Neural Network (NN) − Machine Learning (ML)
- 11. SC development history
- 12. References • Zadeh L. A. Soft Computing and Fuzzy Logic. IEEE Software 11 (6): 48-58, 1998. • Lofti A.Zadeh. what is softcomputing”, softcomputing. Springer- Verlag Germany/ USA, 1997. • Rajasekaran S., G. A Vijayalaksmi Pai. Neural Network, Fuzzy Logic, and Genetic Algorithms, Prentice Hall, 2005. • K. Naresh, Sinha, M. Gupta. Soft Computing and Intelligent Systems – Theory and Applications, Academic Press, 2000. • Fahreddine Karray. Soft Computing and Intelligent System Design – Theory, Tools and Applications, Addison Weslay, 2004. • Tettamanzi, Andrea, Tomassine. Soft Computing: Integrating Evolutionary, Neural and Fuzzy Systems, Springer, 2001. • J. S. R Jang, C. T. Sun. Neuro-Fuzzy and SoftComputing: A Computational Approach to Learning and Machine Intelligance, Prentice Hall, 1996.

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