Introduction to Softcomputing Son Kuswadi Robotic and Automation Based on Biologically- inspired Technology (RABBIT) Electronic Engineering Polytechnic.

Presentasi berjudul: "Introduction to Softcomputing Son Kuswadi Robotic and Automation Based on Biologically- inspired Technology (RABBIT) Electronic Engineering Polytechnic."— Transcript presentasi:

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 Interface Inference Engine Explanation Facility Knowledge Acquisition KB: Fact rules Global Database Knowledge Engineer Human Expert Question Response Expert Systems User

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 cut knowledge Animal? cat Neural character recognition

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 Sensing Devices (Vision) Natural Language Processor Mechanical Devices Perceptions Actions Task Generator Knowledge Handler Data Handler Knowledge Base Machine Learning Inferencing (Reasoning) Planning

9 Neural Networks

10 f   z -1 00 11  N   11 + - e(k+1) 00 ^ ^ y p (k+1) ^ u(k) Parameter Identification - Parallel

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

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

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

14 Neural Networks

15 Experimental Results Feedback control only Feedback with fixed gain feedforward control Feedback with ANN Feedforward controller

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 NoName Height (cm) Degree of Membership of “tall men” CrispFuzzy 1Boy20611 2Martin19011 3Dewanto17500.8 4Joko16000.7 5Kom15500.4

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

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 f A (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 Fuzzy Sets Theory Membership function Kecepatan (km/jam) 0 20 40 6080 Sangat Lambat Lambat Cukup Cepat Cepat Sekali Jarak (m) 0 1 2 3 4 Sangat Dekat Agak Dekat Sedang Agak Jauh Jauh Sekali Posisi pedal rem ( 0 ) 0 10 20 30 40 Injak Penuh Injak Agak Penuh Injak Sedang Injak Sedikit Injak Sedikit Sekali KM JM PPR

26 Fuzzy Sets Theory Fuzzy Rules Aturan 1: Bila kecepatan mobil cepat sekali dan jaraknya sangat dekat maka pedal rem diinjak penuh Aturan 2: Bila kecepatan mobil cukup dan jaraknya agak dekat maka pedal rem diinjak sedang Aturan 3: Bila kecepatan mobil cukup dan jaraknya sangat dekat maka pedal rem diinjak agak penuh

27 Fuzzy Sets Theory Fuzzy Expert Systems Aturan 1: Kecepatan (km/jam) 0 20 40 60 80 Cepat Sekali Posisi pedal rem ( 0 ) 0 10 2030 40 Injak Penuh Jarak (m) 0 1 2 3 4 Sangat Dekat

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

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

30 Fuzzy Sets Theory Fuzzy Expert Systems MOM : PPR = 20 0 10x0,2+20x0,4 COA : PPR = 0,2+0,4 = 16,67 0 Posisi pedal rem ( 0 ) 0 10 20 30 40 MOM COA