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Explore AI, neural networks, fuzzy set theory, and evolutionary computation in robotics. Learn through examples and expert insights on biologically inspired technology for intelligent systems.
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Introduction to Softcomputing Son Kuswadi Robotic and Automation Based on Biologically-inspired Technology (RABBIT) Electronic Engineering Polytechnic Institute of Surabaya Institut Teknologi Sepuluh Nopember
Agenda • AI and Softcomputing • From Conventional AI to Computational Intelligence • Neural Networks • Fuzzy Set Theory • Evolutionary Computation
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
AI and Softcomputing ANN Learning and adaptation Fuzzy Set Theory Knowledge representation Via Fuzzy if-then RULE Genetic Algorithms Systematic Random Search
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
AI and Softcomputing cat Animal? cat cut Neural character recognition knowledge
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)
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
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
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
Neural Networks • Control Learning Error Feedforward controller ANN - ANN + Plant + + + Gp(s) C(s) Gc(s) R(s) - Feedback controller
Neural Networks • Control Current-driven magnetic field Controller Iron ball Ball-position sensor
Neural Networks • Experimental Results Feedback with ANN Feedforward controller Feedback control only Feedback with fixed gain feedforward control
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
Fuzzy Set Theory • Communication of “fuzzy “ idea This box is too heavy.. Therefore, we need a lighter one…
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
Fuzzy Sets Theory • Prof. Lotfi Zadeh
Fuzzy Sets Theory • Classical Set vs Fuzzy set
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
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
Fuzzy Sets Theory • Membership function
Fuzzy Sets Theory • Fuzzy Expert Systems Kecepatan (KM) Jarak (JM) Posisi Pedal Rem (PPR)
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
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
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)
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)
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)
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)