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Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
Introduction to soft computing
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Introduction to soft computing

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Soft Computing For Neural Networks

Soft Computing For Neural Networks

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  • 1. INTRODUCTION TO x it SOFT COMPUTING i D inku R a & Sachin Lakra, r Assistant Professor & Head, Lak Department of IT/MCA hi n Rinku Dixit, cSa Assistant Professor, Department of IT/MCA Manav Rachna College of Engineering 1
  • 2. Contents Intelligent systems ix it D Soft computing inku R Application areas of soft computing & ra L ak in ch Sa 2
  • 3. Traditions in human sciences ix it D Materialism Mathematics & bivalent inku logic R a &Natural sciences r Empiricism Rationalism ak Positivism L Hermeneutics n etc. c hiHuman sciences Human sciences Sa (quantitative) (qualitative) 3
  • 4. Intelligent systems (ISs)Intelligence: System must ix itperform meaningful operations. Dinterpret information. inku R &comprehend the relations between phenomena or objects. a r to new conditions. akapply the acquired information L in ch Sa 4
  • 5. Short-Term Objectives of ISs Everyday routine tasks of human ix it beings: vision, language processing, u D common sense reasoning, learning, robotics. i nk Artificial routine tasks & R identified and developed a rgames, mathematics, logic, by human beings: programming. L ak in developed by human beings: ch Expert tasks Sa Physicists, Mechanical Engineers, Doctors, accountants, other specialisations. 5
  • 6. Long Term Objectives of ISsObjectives: To develop a system whichix it D ku can in essence be a replacement for human in R beings in difficult situations & ra can be physically merged with human Lak beings to replace failed body parts or to in ch create cyborgs Sa 6
  • 7. Cyborgs Mostly Sci-fi ix it D inku R a & r Lak hi n cSa 7
  • 8. Traditional approaches  Mathematical ix it models: Black boxes,D u number i nk crunching. R &  Rule-based systems ra(crisp & bivalent): Lak Large rule bases. hi n cSa 8
  • 9. Soft computing (SC) Objective: ix it D reasoning ku Mimic human (linguistic) in R & ra Main constituents:  Lak systems Fuzzy n hi Neural networks cSa  Evolutionary computing  Probabilistic reasoning 9
  • 10. Soft Computing:DefinitionSoft computing is a term applied ix it a field to D within computer scienceu which is nk characterized by the use Ri inexact solutions of & to computationally-hard tasks such as the ra ak solution of NP-complete problems, for L which an hin solution cannot be derived exact ac in polynomial time. Sen.wikipedia.org/wiki/Soft_computing 10
  • 11. Hard Computing vs Soft Computing Hard computing ix it D  Real-time constraints n ku  Need of accuracy and precisioniin calculations and R outcomes & ra  Useful in critical systems Soft computing L ak in ch  Soft constraints Sa  Need of robustness rather than accuracy  Useful for routine tasks that are not critical 11
  • 12. Hard Computing vs Soft Computing Soft computing differs from conventional ix it (hard) D computing in that it is tolerant of the following   Imprecision Uncertainty inku  Partial truth, and R  Approximation. a & r ak In effect, the role model for soft computing is the human mind. n L hi The guiding principle of soft computing is: c Sa Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. 12
  • 13. Constituents of SC Fuzzy systems => imprecision ix it D Neural networks => learning in ku R Probabilistic reasoning => uncertainty & ra ak Evolutionary computing => optimization L in a ch 24,000 publications as of today SOver 13
  • 14. SC: a user-friendly approach ix it D inku R Soft computing & approach r a ak Linguistic world Soft data n L Mathematical world Interpretations hi Hard data Understanding Quantitative methods Explanations c Sa Bivalent reasoning Qualitative methods Bivalent or multivalent reasoning Phenomenon under study 14
  • 15. Advantages of SC Models based on human reasoning. ix it D Closer to human thinking inku Models can be R &  linguistic ra ak  simple (no number crunching), L in  comprehensible (no black boxes), ch computing, Sa  fast when  effective in practice. 15
  • 16. SC today (Zadeh) Computing with words (CW) ix it D ku Theory of information granulation in (TFIG) R & ra Computational theory of perceptions (CTP) Lak in ch Sa 16
  • 17. Possible SC data & operations Numeric data: ix it D 5, about 5, 5 to 6, about 5 to ku6 Linguistic data: Rin & medium or bad cheap, very big, notahigh, r Functions & L ak relations: n if(x), fairly similar, much greater ch f(x), about Sa 17
  • 18. Neural networks (NN, 1940s)  x it Neural networks offer imethod to D a powerful ku explore, classify, and in patterns in R identify & data. ra  Neurons L ak Neuron: y=Σwixi nInputs Outputs hi (1 layer) c Sa Walter Pitts Warren S. McCulloch 18
  • 19. Machine learning (supervised) ix it  Pattern recognition Orange based u D on training i nk data. R Instructor a & Classification  r Lak supervised by instructor. hi n c  Neural (crisp or? Sa Apple fuzzy), neuro-fuzzy and fuzzy models. 19
  • 20. Machine learning (unsupervised)  ix it Pattern recognitionOrange based u D on training nk data. i Mango  R Classification based & on structure of data ra (clustering). Lak Apple in  No instructor a ch  Neural (crisp or S fuzzy), neuro-fuzzy Labeling and fuzzy models. 20
  • 21. Fuzzy systems (Zadeh, 1960s) (computer environments) ix it Deal with imprecise entities in automated environments D inku Based on fuzzy set theory and fuzzy logic. R Most applications in control and decision making a & r Lak hi n c Sa Omron’s fuzzy processorLotfi A. Zadeh 21
  • 22. SC applications: control  Heavy industry ix it  Matsushita, Siemens  robotic arms, humanoid robots  u D Home appliances  k  Canon, Sony, Goldstar, Siemens n refrigerators, iAutomobiles cameras washing machines, ACs, R  a &  Nissan, Mitsubishi, Daimler- r Chrysler, BMW, Volkswagen Lak  Travel Speed Estimation, Sleep Warning Systems, Driver-less cars hi n  Spacecrafts  NASA cSa  Manoeuvering of a Space Shuttle(FL), Optimization of Fuel- efficient Solutions for a Manoeuvre(GA), Monitoring and Diagnosis of Degradation of Components and Subsystems(FL), Virtual Sensors(ANN) 22
  • 23. SC applications: business supplier evaluation for  hospital stay ix it prediction, D kusample testing,  TV commercial slot customer targeting, sequencing, R in matching, evaluation,  address scheduling, & fuzzy cluster analysis, a  r optimizing R&D projects, Lak  sales prognosis for mail order house, knowledge-based hi n (source: FuzzyTech) cprognosis,Sa fuzzy data analysis 23
  • 24. SC applications: finance Fuzzy scoring for mortgage applicants, ix it creditworthiness assessment, D ku fuzzy-enhanced score card for lease risk assessment, in risk profile analysis, R insurance fraud detection, a & r ak cash supply optimization, L hi n foreign exchange trading, c Sa insider trading surveillance, investor classification etc.Source: FuzzyTech 24
  • 25. SC applications: robotics ix it D inku R a & r Lak hi n cSa 25
  • 26. SC applications: others ix it Statistics D Social sciences inku R Behavioural sciences a & r Biology Lak Medicine hi n c Sa 26
  • 27. (Neuro)-fuzzy system construction it ixExperts Training Fuzzy rules D data (SOM, c-means ku etc.) Rin & raControl ak System Levaluation Tuning (NN)data in ch (errors) Sa New system 27
  • 28. Model construction (mathematical)  Mathematical models are functions. Deep knowledge on mathematics. ix it  D If non-linear (eg. NN), laborious calculations and computing.  Linear models can be too simplified. inku  How can we find appropriate functions? R 1,2 a & r Lak 1 hi n 0,8 cY=1-1./(1 + EXP(-2*(X-5))) Sa 0,6 Y 0,4 0,2 0 0 2 4 6 8 10 12 X 28
  • 29. Model construction (trad. rules )If 0<x<1, then y=1  ix it Rule for each input. => Large rule bases.  Only one rule is fired for each input.If 1<x<2, then y=0.99 D ku:  Coarse models.If 8<x<10, then y=0 1,2 Rin 1 a & r ak 0,8If 0<x<1, then y=f(x)If 1<x<2, then y=g(x) n L 0,6 Y: c hi 0,4 SaIf 8<x<10, then y=h(x) 0,2 0 0 2 4 6 8 10 12 X 29
  • 30. Model construction (SC/fuzzy) Approximate values=> Small rule bases. ix it Rules only describe typical cases (no rule for each input). D ku A group of rules are partially fired simultaneously. in R & 1,2If x≈0, then y≈1 r a ak 1If x≈5, then y≈0.5 L 0,8If x≈10, then y≈0 hi n 0,6 Y c Sa 0,4 0,2 0 0 2 4 6 8 10 12 X 30
  • 31. SC and future ix it beSC and conventional methods should Dused in combination. inku R & ra Lak in ch Sa 31
  • 32. Sources of SC Books: ix it D ku www.springer.de/cgi-bin/search_book.pl?series=2941, www.elsevier.com/locate/fss, Rin www.springer.de/cgi-bin/search_book.pl?series=4240, a & www.wkap.nl r Others: Lak hi n http://http.cs.berkeley.edu/projects/Bisc/bisc.memo.html c Sa 32
  • 33. References it ix New York,1. D J. Bezdek & S. Pal, Fuzzy models for pattern recognition (IEEE Press,2. 1992). in L. Zadeh, Fuzzy logic = Computing with words, IEEE ku Transactions on Fuzzy L. Zadeh, From Computing with Numbers RComputing with Words -- From Systems, vol. 2, pp. 103-111, 1996.3. & to on Circuits and Systems, 45, 1999, ra Manipulation of Measurements to Manipulation of Perceptions, IEEE Transactions L. Zadeh, Toward a theory of k 105-119.4. a fuzzy information granulation (1997)its111-127. in and centrality L theory and its applications (Kluwer, Dordrecht, 1991). human reasoning and fuzzy logic, Fuzzy Sets and Systems 90/2 in ch5. H.-J. Zimmermann, Fuzzy set Sa 33

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