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Introduction to Softcomputing

Introduction to Softcomputing

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Introduction to Softcomputing

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  1. Introduction to Softcomputing Son Kuswadi Robotic and Automation Based on Biologically-inspired Technology (RABBIT) Electronic Engineering Polytechnic Institute of Surabaya Institut Teknologi Sepuluh Nopember

  2. Agenda • AI and Softcomputing • From Conventional AI to Computational Intelligence • Neural Networks • Fuzzy Set Theory • Evolutionary Computation

  3. AI and Softcomputing • AI: predicate logic and symbol manipulation techniques User Global Database Inference Engine Question User Interface Explanation Facility KB: • Fact • rules Response Knowledge Acquisition Knowledge Engineer Human Expert Expert Systems

  4. AI and Softcomputing ANN Learning and adaptation Fuzzy Set Theory Knowledge representation Via Fuzzy if-then RULE Genetic Algorithms Systematic Random Search

  5. AI and Softcomputing ANN Learning and adaptation Fuzzy Set Theory Knowledge representation Via Fuzzy if-then RULE Genetic Algorithms Systematic Random Search AI Symbolic Manipulation

  6. AI and Softcomputing cat Animal? cat cut Neural character recognition knowledge

  7. From Conventional AI to Computational Intelligence • Conventional AI: • Focuses on attempt to mimic human intelligent behavior by expressing it in language forms or symbolic rules • Manipulates symbols on the assumption that such behavior can be stored in symbolically structured knowledge bases (physical symbol system hypothesis)

  8. From Conventional AI to Computational Intelligence • Intelligent Systems Machine Learning Sensing Devices (Vision) Perceptions Task Generator Inferencing (Reasoning) Natural Language Processor Planning Knowledge Handler Knowledge Base Mechanical Devices Actions Data Handler

  9. Neural Networks

  10. Neural Networks yp(k+1) f  z-1 0 - z-1 u(k)   e(k+1) 1 + ^ yp(k+1) N  z-1 ^ 0 z-1  ^ 1 Parameter Identification - Parallel

  11. Neural Networks yp(k+1) f  z-1 0 - z-1 u(k)   e(k+1) 1 + ^ yp(k+1) N  z-1 ^ 0 z-1  ^ 1 Parameter Identification – Series Parallel

  12. Neural Networks • Control Learning Error Feedforward controller ANN - ANN + Plant + + + Gp(s) C(s) Gc(s) R(s) - Feedback controller

  13. Neural Networks • Control Current-driven magnetic field Controller Iron ball Ball-position sensor

  14. Neural Networks

  15. Neural Networks • Experimental Results Feedback with ANN Feedforward controller Feedback control only Feedback with fixed gain feedforward control

  16. Fuzzy Sets Theory • What is fuzzy thinking • Experts rely on common sense when they solve the problems • How can we represent expert knowledge that uses vague and ambiguous terms in a computer • Fuzzy logic is not logic that is fuzzy but logic that is used to describe the fuzziness. Fuzzy logic is the theory of fuzzy sets, set that calibrate the vagueness. • Fuzzy logic is based on the idea that all things admit of degrees. Temperature, height, speed, distance, beauty – all come on a sliding scale. Jim is tall guy It is really very hot today

  17. Fuzzy Set Theory • Communication of “fuzzy “ idea This box is too heavy.. Therefore, we need a lighter one…

  18. Fuzzy Sets Theory • Boolean logic • Uses sharp distinctions. It forces us to draw a line between a members of class and non members. • Fuzzy logic • Reflects how people think. It attempt to model our senses of words, our decision making and our common sense -> more human and intelligent systems

  19. Fuzzy Sets Theory • Prof. Lotfi Zadeh

  20. Fuzzy Sets Theory • Classical Set vs Fuzzy set

  21. Fuzzy Sets Theory • Classical Set vs Fuzzy set Membership value Membership value 1 1 0 0 175 Height(cm) 175 Height(cm) Universe of discourse

  22. Fuzzy Sets Theory • Classical Set vs Fuzzy set Let X be the universe of discourse and its elements be denoted as x. In the classical set theory, crisp set A of X is defined as function fA(x) called the the characteristic function of A In the fuzzy theory, fuzzy set A of universe of discourse X is defined by function called the membership function of set A

  23. Fuzzy Sets Theory • Membership function

  24. Fuzzy Sets Theory • Fuzzy Expert Systems Kecepatan (KM) Jarak (JM) Posisi Pedal Rem (PPR)

  25. Injak Penuh Injak Sedang Injak Sedikit Injak Agak Penuh Injak Sedikit Sekali Sangat Lambat Cukup Sangat Dekat Sedang Cepat Lambat Agak Jauh Agak Dekat Cepat Sekali 0 20 40 60 80 0 10 20 30 40 0 1 2 3 4 Jauh Sekali Posisi pedal rem (0) Kecepatan (km/jam) Jarak (m) Fuzzy Sets Theory • Membership function PPR JM KM

  26. Fuzzy Sets Theory • Fuzzy Rules Aturan 1: Bila kecepatan mobil cepat sekalidan jaraknya sangat dekatmaka pedal rem diinjak penuh Aturan 2: Bila kecepatan mobil cukupdan jaraknya agak dekatmaka pedal rem diinjak sedang Aturan 3: Bila kecepatan mobil cukupdan jaraknya sangat dekatmaka pedal rem diinjak agak penuh

  27. Injak Penuh 0 10 20 30 40 Posisi pedal rem (0) Fuzzy Sets Theory • Fuzzy Expert Systems Aturan 1: Cepat Sekali Sangat Dekat 0 20 40 60 80 0 1 2 3 4 Kecepatan (km/jam) Jarak (m)

  28. Injak Sedang Agak Dekat 0 10 20 30 40 Posisi pedal rem (0) 0 1 2 3 4 Jarak (m) Fuzzy Sets Theory • Fuzzy Expert Systems Aturan 2: Cukup 0 20 40 60 80 Kecepatan (km/jam)

  29. Sangat Dekat 0 1 2 3 4 0 10 20 30 40 Jarak (m) Injak Agak Penuh Posisi pedal rem (0) Fuzzy Sets Theory • Fuzzy Expert Systems Aturan 3: Cukup 0 20 40 60 80 Kecepatan (km/jam)

  30. Fuzzy Sets Theory • Fuzzy Expert Systems MOM : PPR = 200 10x0,2+20x0,4 COA : PPR = 0,2+0,4 = 16,670 MOM COA 0 10 20 30 40 Posisi pedal rem (0)