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EasyMedico.com
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Soft computing aims to mimic human reasoning using techniques like fuzzy logic, neural networks, evolutionary computing and probabilistic reasoning. It has advantages over conventional computing by producing models that are linguistically simple, comprehensible, fast and effective in practice. Soft computing has over 24,000 publications and is used widely in applications such as control systems, business, finance, robotics, science and more.

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Soft computing (SC) Objective: Mimic human (linguistic) reasoning Main constituents: - Fuzzy systems - Neural networks - Evolutionary computing - Probabilistic reasoning VAN-00
Constituents of SC Fuzzy systems => imprecision Neural networks => learning Probabilistic reasoning => uncertainty Evolutionary computing => optimization VAN-00 Over 24 000 publications today
SC: a user-friendly interface VAN-00
Advantages of SC Models base on human reasoning. Models can be - linguistic - simple (no number crunching), - comprehensible (no black boxes), - fast when computing, - good in practice. VAN-00
SC today (Zadeh) Computing with words (CW) Theory of information granulation (TFIG) Computational theory of perceptions (CTP) VAN-00
Possible SC data & operations Numeric data: 5, about 5, 5 to 6, about 5 to 6 Linguistic data: cheap, very big, not high, medium or bad Functions & relations: f(x), about f(x), fairly similar, much greater VAN-00
Neural networks (NN, 1940's) Neural networks offer a powerful method to explore, classify, and identify patterns in data. Website of Matlab Neuron: y=  w i x i VAN-00
Machine learning (supervised) Pattern recognition based on training data. Classification supervised by instructor. Neural (crisp or fuzzy), neuro-fuzzy and fuzzy models. VAN-00 Peach Plum ? Instructor
Machine learning (unsupervised) Pattern recognition based on training data. Classification based on structure of data (clustering). Neural (crisp or fuzzy), neuro-fuzzy and fuzzy models. VAN-00 Peach Plum Nectarine Labeling
Machine learning (unsupervised) Self-organized maps (Kohonen). Fuzzy c-means (Bezdek). Subclust (Yager, Chiu). VAN-00 Peach Plum Nectarine Labeling Websom Self-Organizing Maps for Internet Exploration
Fuzzy systems (Zadeh, 1960's) Deal with imprecise entities in automated environments (computer environments) Base on fuzzy set theory and fuzzy logic. Most applications in control and decision making VAN-00 Omron’s fuzzy processor Omron Electronics Matlab's Fuzzy Logic Toolbox
SC applications: control Heavy industry (Matsushita, Siemens, Stora-Enso) Home appliances (Canon, Sony, Goldstar, Siemens) Automobiles (Nissan, Mitsubishi, Daimler-Chrysler, BMW, Volkswagen) Spacecrafts (NASA) VAN-00
SC applications: business VAN-00 hospital stay prediction, TV commercial slot evaluation, address matching, fuzzy cluster analysis, sales prognosis for mail order house, multi-criteria optimization etc. (source: FuzzyTech) supplier evaluation for sample testing, customer targeting, sequencing, scheduling, optimizing R&D projects, knowledge-based prognosis, fuzzy data analysis
SC applications: finance Fuzzy scoring for mortgage applicants, creditworthiness assessment, fuzzy-enhanced score card for lease risk assessment, risk profile analysis, insurance fraud detection, cash supply optimization, foreign exchange trading, insider trading surveillance, investor classification etc. Source: FuzzyTech VAN-00
SC applications: robotics VAN-00 Fukuda’s lab Joseph F. Engelberger We are proud to announce that the HelpMate Robotic Courier has been acquired by Pyxis Corporation . Entertainment robot AIBO
SC applications: others VAN-00 Statistics Social sciences Behavioural sciences Biology Medicine
(Neuro)-fuzzy system construction VAN-00 Training data Experts Fuzzy rules (SOM, c-means etc.) Control data System evaluation (errors) Tuning (NN) New system
Model construction (mathematical) Mathematical models are functions. Deep knowledge on mathematics. If non-linear (eg. NN), laborious calculations and computing. Linear models can be too simplified. How can we find appropriate functions? VAN-00 Y=1-1./(1 + EXP(-2*(X-5)))
Model construction (trad. rules ) VAN-00 If 0 < x<1, then y=1 If 1 < x<2, then y=0.99 : If 8 < x < 10, then y=0 If 0 < x<1, then y=f(x) If 1 < x<2, then y=g(x) : If 8 < x < 10, then y=h(x) - Rule for each input. => Large rule bases. - Only one rule is fired for each input. - Coarse models.
Model construction (SC/fuzzy) VAN-00 If x  0, then y  1 If x  5, then y  0.5 If x  10, then y  0 - Approximate values - Rules only describe typical cases (no rule for each input). => Small rule bases. - A group of rules are partially fired simultaneously.
SC and future SC and conventional methods should be used in combination. VAN-00

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Nis1

  • 1. Soft computing (SC) Objective: Mimic human (linguistic) reasoning Main constituents: - Fuzzy systems - Neural networks - Evolutionary computing - Probabilistic reasoning VAN-00
  • 2. Constituents of SC Fuzzy systems => imprecision Neural networks => learning Probabilistic reasoning => uncertainty Evolutionary computing => optimization VAN-00 Over 24 000 publications today
  • 3. SC: a user-friendly interface VAN-00
  • 4. Advantages of SC Models base on human reasoning. Models can be - linguistic - simple (no number crunching), - comprehensible (no black boxes), - fast when computing, - good in practice. VAN-00
  • 5. SC today (Zadeh) Computing with words (CW) Theory of information granulation (TFIG) Computational theory of perceptions (CTP) VAN-00
  • 6. Possible SC data & operations Numeric data: 5, about 5, 5 to 6, about 5 to 6 Linguistic data: cheap, very big, not high, medium or bad Functions & relations: f(x), about f(x), fairly similar, much greater VAN-00
  • 7. Neural networks (NN, 1940's) Neural networks offer a powerful method to explore, classify, and identify patterns in data. Website of Matlab Neuron: y=  w i x i VAN-00
  • 8. Machine learning (supervised) Pattern recognition based on training data. Classification supervised by instructor. Neural (crisp or fuzzy), neuro-fuzzy and fuzzy models. VAN-00 Peach Plum ? Instructor
  • 9. Machine learning (unsupervised) Pattern recognition based on training data. Classification based on structure of data (clustering). Neural (crisp or fuzzy), neuro-fuzzy and fuzzy models. VAN-00 Peach Plum Nectarine Labeling
  • 10. Machine learning (unsupervised) Self-organized maps (Kohonen). Fuzzy c-means (Bezdek). Subclust (Yager, Chiu). VAN-00 Peach Plum Nectarine Labeling Websom Self-Organizing Maps for Internet Exploration
  • 11. Fuzzy systems (Zadeh, 1960's) Deal with imprecise entities in automated environments (computer environments) Base on fuzzy set theory and fuzzy logic. Most applications in control and decision making VAN-00 Omron’s fuzzy processor Omron Electronics Matlab's Fuzzy Logic Toolbox
  • 12. SC applications: control Heavy industry (Matsushita, Siemens, Stora-Enso) Home appliances (Canon, Sony, Goldstar, Siemens) Automobiles (Nissan, Mitsubishi, Daimler-Chrysler, BMW, Volkswagen) Spacecrafts (NASA) VAN-00
  • 13. SC applications: business VAN-00 hospital stay prediction, TV commercial slot evaluation, address matching, fuzzy cluster analysis, sales prognosis for mail order house, multi-criteria optimization etc. (source: FuzzyTech) supplier evaluation for sample testing, customer targeting, sequencing, scheduling, optimizing R&D projects, knowledge-based prognosis, fuzzy data analysis
  • 14. SC applications: finance Fuzzy scoring for mortgage applicants, creditworthiness assessment, fuzzy-enhanced score card for lease risk assessment, risk profile analysis, insurance fraud detection, cash supply optimization, foreign exchange trading, insider trading surveillance, investor classification etc. Source: FuzzyTech VAN-00
  • 15. SC applications: robotics VAN-00 Fukuda’s lab Joseph F. Engelberger We are proud to announce that the HelpMate Robotic Courier has been acquired by Pyxis Corporation . Entertainment robot AIBO
  • 16. SC applications: others VAN-00 Statistics Social sciences Behavioural sciences Biology Medicine
  • 17. (Neuro)-fuzzy system construction VAN-00 Training data Experts Fuzzy rules (SOM, c-means etc.) Control data System evaluation (errors) Tuning (NN) New system
  • 18. Model construction (mathematical) Mathematical models are functions. Deep knowledge on mathematics. If non-linear (eg. NN), laborious calculations and computing. Linear models can be too simplified. How can we find appropriate functions? VAN-00 Y=1-1./(1 + EXP(-2*(X-5)))
  • 19. Model construction (trad. rules ) VAN-00 If 0 < x<1, then y=1 If 1 < x<2, then y=0.99 : If 8 < x < 10, then y=0 If 0 < x<1, then y=f(x) If 1 < x<2, then y=g(x) : If 8 < x < 10, then y=h(x) - Rule for each input. => Large rule bases. - Only one rule is fired for each input. - Coarse models.
  • 20. Model construction (SC/fuzzy) VAN-00 If x  0, then y  1 If x  5, then y  0.5 If x  10, then y  0 - Approximate values - Rules only describe typical cases (no rule for each input). => Small rule bases. - A group of rules are partially fired simultaneously.
  • 21. SC and future SC and conventional methods should be used in combination. VAN-00