nis1
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VAN-00 1
Soft computing (SC)
Objective:
Mimic human (linguistic) reasoning
Main constituents:
- Fuzzy systems
- Neural networks
- Evolutionary computing
- Probabilistic reasoning
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VAN-00 2
Constituents of SC
• Fuzzy systems => imprecision
• Neural networks => learning
• Probabilistic reasoning => uncertainty
• Evolutionary computing => optimization
Over 24 000 publications today
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VAN-00 3
SC: a user-friendly interface
Soft computing approach
Linguistic worldSoft data
InterpretationsUnderstandingExplanations
Qualitative methodsBivalent or multivalent
reasoning
Mathematical worldHard data
Quantitative methodsBivalent reasoning
Phenomenon under study
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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.
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VAN-00 5
SC today (Zadeh)
• Computing with words (CW)
• Theory of information granulation (TFIG)
• Computational theory of perceptions (CTP)
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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
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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=wixi
InputsNeurons(1 layer)
Outputs
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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.
Peach
Plum?
Instructor
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VAN-00 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.
Peach
Plum
Nectarine
Labeling
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VAN-00 10
Machine learning (unsupervised)
• Self-organized maps (Kohonen).
• Fuzzy c-means (Bezdek).
• Subclust (Yager, Chiu).
Peach
Plum
Nectarine
LabelingWebsomSelf-Organizing
Maps for Internet Exploration
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VAN-00 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
Omron’s fuzzy processor
Omron Electronics
Matlab's Fuzzy Logic Toolbox
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VAN-00 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)
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VAN-00 13
SC applications: business
•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
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VAN-00 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
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VAN-00 15
SC applications: robotics
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
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VAN-00 16
SC applications: others
•Statistics
•Social sciences
•Behavioural sciences
•Biology
•Medicine
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VAN-00 17
(Neuro)-fuzzy system construction
Training data
ExpertsFuzzy rules(SOM, c-means etc.)
Control data
System evaluation(errors)
Tuning(NN)
New system
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VAN-00 180
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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?
Y=1-1./(1 + EXP(-2*(X-5)))
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VAN-00 19
Model construction (trad. rules )
If 0<x<1, then y=1If 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)
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- Rule for each input. => Large rule bases.- Only one rule is fired for each input. - Coarse models.
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VAN-00 20
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Model construction (SC/fuzzy)
If x0, then y1If x5, then y0.5If x10, then y0
- Approximate values- Rules only describe typical cases (no rule for each input). => Small rule bases.- A group of rules are partially fired simultaneously.
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VAN-00 21
SC and future
SC and conventional methods should be used in combination.