Chap 3: Fuzzy Rules and Fuzzy Reasoning J.-S. Roger Jang ( 張智星 ) CS Dept., Tsing Hua Univ., Taiwan Fuzzy.

Slides:



Advertisements
Similar presentations
Fuzzy Inference and Defuzzification
Advertisements

Fuzzy Logic The restriction of classical propositional calculus to a two- valued logic has created many interesting paradoxes over the ages. For example,
Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing J.-S. Roger Jang ( 張智星 ) CS Dept., Tsing Hua Univ., Taiwan
CLASSICAL LOGIC and FUZZY LOGIC. CLASSICAL LOGIC In classical logic, a simple proposition P is a linguistic, or declarative, statement contained within.
Fuzzy Expert System Fuzzy Logic
Fuzzy Expert System. Basic Notions 1.Fuzzy Sets 2.Fuzzy representation in computer 3.Linguistic variables and hedges 4.Operations of fuzzy sets 5.Fuzzy.
Fuzzy Expert Systems. Lecture Outline What is fuzzy thinking? What is fuzzy thinking? Fuzzy sets Fuzzy sets Linguistic variables and hedges Linguistic.
FUZZY SYSTEMS. Fuzzy Systems Fuzzy Sets – To quantify and reason about fuzzy or vague terms of natural language – Example: hot, cold temperature small,
Approximate Reasoning 1 Expert Systems Dr. Samy Abu Nasser.
© C. Kemke Approximate Reasoning 1 COMP 4200: Expert Systems Dr. Christel Kemke Department of Computer Science University of Manitoba.
Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing J.-S. Roger Jang ( 張智星 ) CS Dept., Tsing Hua Univ., Taiwan
Fuzzy Control Lect 3 Membership Function and Approximate Reasoning
Fuzzy Expert System.
Neuro-Fuzzy Control Adriano Joaquim de Oliveira Cruz NCE/UFRJ
PART 8 Approximate Reasoning 1. Fuzzy expert systems 2. Fuzzy implications 3. Selecting fuzzy implications 4. Multiconditional reasoning 5. Fuzzy relation.
Linguistic Descriptions Adriano Joaquim de Oliveira Cruz NCE e IM/UFRJ © 2003.
COMP 578 Fuzzy Sets in Data Mining Keith C.C. Chan Department of Computing The Hong Kong Polytechnic University.
Ming-Feng Yeh General Fuzzy Systems A fuzzy system is a static nonlinear mapping between its inputs and outputs (i.e., it is not a dynamic system).

Dan Simon Cleveland State University
Fuzzy Sets and Fuzzy Logic Theory and Applications
Fuzzy Rule-based Models *Neuro-fuzzy and Soft Computing - J.Jang, C. Sun, and, E. Mizutani, Prentice Hall 1997.
BEE4333 Intelligent Control
Rule-Based Fuzzy Model. In rule-based fuzzy systems, the relationships between variables are represented by means of fuzzy if–then rules of the following.
FUZZY LOGIC Babu Appat. OVERVIEW What is Fuzzy Logic? Where did it begin? Fuzzy Logic vs. Neural Networks Fuzzy Logic in Control Systems Fuzzy Logic in.
Fuzzy Logic Conception Introduced by Lotfi Zadeh in 1960s at Berkley Wanted to expand crisp logic.
Fuzzy Expert Systems. 2 Fuzzy Logic Four types of fuzzy logics Classic logic Crisp setence: Height(John, 180) → Weight(John, 60) Crisp data: Height(John,
Increasing/ Decreasing
Theory and Applications
Fuzzy Inference Systems. Fuzzy inference (reasoning) is the actual process of mapping from a given input to an output using fuzzy logic. The process involves.
Lecture 3 Fuzzy Reasoning 1. inference engine core of every fuzzy controller the computational mechanism with which decisions can be inferred even though.
CHAPTER 3 FUZZY RELATION and COMPOSITION. 3.1 Crisp relation Product set Definition (Product set) Let A and B be two non-empty sets, the product.
“Principles of Soft Computing, 2 nd Edition” by S.N. Sivanandam & SN Deepa Copyright  2011 Wiley India Pvt. Ltd. All rights reserved. CHAPTER 12 FUZZY.
Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing Provided: J.-S. Roger Jang Modified: Vali Derhami Neuro-Fuzzy and Soft Computing: Fuzzy.
Fuzzy systems. Calculate the degree of matching Fuzzy inference engine Defuzzification module Fuzzy rule base General scheme of a fuzzy system.
Fuzzy Inference and Reasoning
Basic Concepts of Fuzzy Logic Apparatus of fuzzy logic is built on: Fuzzy sets: describe the value of variables Linguistic variables: qualitatively and.
Chapter 4: Fuzzy Inference Systems Introduction (4.1) Mamdani Fuzzy models (4.2) Sugeno Fuzzy Models (4.3) Tsukamoto Fuzzy models (4.4) Other Considerations.
Lecture 32 Fuzzy Set Theory (4)
The Distributive Property Chapter 1.6. Review multiplication.
1 Vagueness The Oxford Companion to Philosophy (1995): “Words like smart, tall, and fat are vague since in most contexts of use there is no bright line.
Fuzzy Expert System n Introduction n Fuzzy sets n Linguistic variables and hedges n Operations of fuzzy sets n Fuzzy rules n Summary.
Chapter 10 Fuzzy Control and Fuzzy Expert Systems
Could Be Significant.
Fuzzy Sets Neuro-Fuzzy and Soft Computing: Fuzzy Sets ...
1 Lecture 4 The Fuzzy Controller design. 2 By a fuzzy logic controller (FLC) we mean a control law that is described by a knowledge-based system consisting.
Fuzzy Logic Artificial Intelligence Chapter 9. Outline Crisp Logic Fuzzy Logic Fuzzy Logic Applications Conclusion “traditional logic”: {true,false}
Ch.3 Fuzzy Rules and Fuzzy Reasoning
Distance/Similarity Functions for Pattern Recognition J.-S. Roger Jang ( 張智星 ) CS Dept., Tsing Hua Univ., Taiwan
Fuzzy Relations( 關係 ), Fuzzy Graphs( 圖 形 ), and Fuzzy Arithmetic( 運算 ) Chapter 4.
Chapter 3: Fuzzy Rules & Fuzzy Reasoning Extension Principle & Fuzzy Relations (3.2) Fuzzy if-then Rules(3.3) Fuzzy Reasonning (3.4)
Chapter 8 : Fuzzy Logic.
Chapter 9 Fuzzy Inference
Expert System Structure
Fuzzy Inference Systems
Fuzzy Logic and Approximate Reasoning
Introduction to Fuzzy Logic
Homework 8 Min Max “Temperature is low” AND “Temperature is middle”
Introduction to Fuzzy Logic
Chap 3: Fuzzy Rules and Fuzzy Reasoning
Fuzzy System Structure
Chap 3: Fuzzy Rules and Fuzzy Reasoning
Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing
Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing
Chap 3: Fuzzy Rules and Fuzzy Reasoning
Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing
Fuzzy Sets Neuro-Fuzzy and Soft Computing: Fuzzy Sets ...
Chap 4: Fuzzy Inference Systems
Fuzzy Inference Systems
Introduction to Fuzzy Set Theory
Presentation transcript:

Chap 3: Fuzzy Rules and Fuzzy Reasoning J.-S. Roger Jang ( 張智星 ) CS Dept., Tsing Hua Univ., Taiwan Fuzzy Rules and Fuzzy Reasoning

2 Outline Extension principle Fuzzy relations Fuzzy if-then rules Compositional rule of inference Fuzzy reasoning

Fuzzy Rules and Fuzzy Reasoning 3 Extension Principle A is a fuzzy set on X : The image of A under f( ) is a fuzzy set B: where y i = f(x i ), i = 1 to n. If f( ) is a many-to-one mapping, then

Fuzzy Rules and Fuzzy Reasoning 4 Fuzzy Relations A fuzzy relation R is a 2D MF: Examples: x is close to y (x and y are numbers) x depends on y (x and y are events) x and y look alike (x, and y are persons or objects) If x is large, then y is small (x is an observed reading and Y is a corresponding action)

Fuzzy Rules and Fuzzy Reasoning 5 Max-Min Composition The max-min composition of two fuzzy relations R 1 (defined on X and Y) and R 2 (defined on Y and Z) is Properties: Associativity: Distributivity over union: Week distributivity over intersection: Monotonicity:

Fuzzy Rules and Fuzzy Reasoning 6 Max-Star Composition Max-product composition: In general, we have max-* composition: where * is a T-norm operator.

Fuzzy Rules and Fuzzy Reasoning 7 Linguistic Variables A numerical variables takes numerical values: Age = 65 A linguistic variables takes linguistic values: Age is old A linguistic values is a fuzzy set. All linguistic values form a term set: T(age) = {young, not young, very young,... middle aged, not middle aged,... old, not old, very old, more or less old,... not very yound and not very old,...}

Fuzzy Rules and Fuzzy Reasoning 8 Linguistic Values (Terms) complv.m

Fuzzy Rules and Fuzzy Reasoning 9 Operations on Linguistic Values Concentration: Dilation: Contrast intensification: intensif.m

Fuzzy Rules and Fuzzy Reasoning 10 Fuzzy If-Then Rules General format: If x is A then y is B Examples: If pressure is high, then volume is small. If the road is slippery, then driving is dangerous. If a tomato is red, then it is ripe. If the speed is high, then apply the brake a little.

Fuzzy Rules and Fuzzy Reasoning 11 Fuzzy If-Then Rules A coupled with B AA BB A entails B Two ways to interpret “If x is A then y is B”: y xx y

Fuzzy Rules and Fuzzy Reasoning 12 Fuzzy If-Then Rules Two ways to interpret “If x is A then y is B”: A coupled with B: (A and B) A entails B: (not A or B) -Material implication -Propositional calculus -Extended propositional calculus -Generalization of modus ponens

Fuzzy Rules and Fuzzy Reasoning 13 Fuzzy If-Then Rules Fuzzy implication function: fuzimp.m A coupled with B

Fuzzy Rules and Fuzzy Reasoning 14 Fuzzy If-Then Rules A entails B fuzimp.m

Fuzzy Rules and Fuzzy Reasoning 15 Compositional Rule of Inference Derivation of y = b from x = a and y = f(x): a and b: points y = f(x) : a curve a b y xx y a and b: intervals y = f(x) : an interval-valued function a b y = f(x)

Fuzzy Rules and Fuzzy Reasoning 16 Compositional Rule of Inference a is a fuzzy set and y = f(x) is a fuzzy relation: cri.m

Fuzzy Rules and Fuzzy Reasoning 17 Fuzzy Reasoning Single rule with single antecedent Rule: if x is A then y is B Fact: x is A’ Conclusion: y is B’ Graphic Representation: A X w A’B Y x is A’ B’ Y A’ X y is B’

Fuzzy Rules and Fuzzy Reasoning 18 Fuzzy Reasoning Single rule with multiple antecedent Rule: if x is A and y is B then z is C Fact: x is A’ and y is B’ Conclusion: z is C’ Graphic Representation: AB T-norm XY w A’B’C2C2 Z C’ Z XY A’B’ x is A’y is B’z is C’

Fuzzy Rules and Fuzzy Reasoning 19 Fuzzy Reasoning Multiple rules with multiple antecedent Rule 1: if x is A 1 and y is B 1 then z is C 1 Rule 2: if x is A 2 and y is B 2 then z is C 2 Fact: x is A’ and y is B’ Conclusion: z is C’ Graphic Representation: (next slide)

Fuzzy Rules and Fuzzy Reasoning 20 Fuzzy Reasoning Graphics representation: A1A1 B1B1 A2A2 B2B2 T-norm X X Y Y w1w1 w2w2 A’ B’ C1C1 C2C2 Z Z C’ Z XY A’B’ x is A’y is B’z is C’

Fuzzy Rules and Fuzzy Reasoning 21 Fuzzy Reasoning: MATLAB Demo >> ruleview mam21

Fuzzy Rules and Fuzzy Reasoning 22 Other Variants Some terminology: Degrees of compatibility (match) Firing strength Qualified (induced) MFs Overall output MF