Introduction to soft computing
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Soft Computing For Neural Networks

Soft Computing For Neural Networks

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Introduction to soft computing Presentation Transcript

  • 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