What is all the Fuzz about?
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1 What is all the Fuzz about? Fuzzy Systems: Introduction CPSC 533 Christian Jacob Dept. of Computer Science Dept. of Biochemistry & Molecular Biology University of Calgary
2 Fuzzy Systems in Knowledge Engineering
3 Fuzzy Systems 1. Motivation 2. Fuzzy Sets 3. Fuzzy Numbers 4. Fuzzy Sets and Fuzzy Rules 5. Extracting Fuzzy Models from Data 6. Examples of Fuzzy Systems
4 What does Fuzzy Logic mean? Fuzzy logic was introduced by Lotfi Zadeh UC Berkeley in Fuzzy logic is based on fuzzy set theory, an extension of classical set theory. Fuzzy logic attempts to formalize approximate knowledge and reasoning. Fuzzy logic did not attract any attention until the 1980s fuzzy controller applications.
5 Humans primarily use fuzzy terms: large, small, fast, slow, warm, cold,... We say: If the weather is nice and I have a little time, I will probably go for a hike along the Bow. We don t say: Fuzzy is Just Human If the temperature is above 24 degrees and the cloud cover is less than 10, and I have 3 hours time, I will go for a hike with a probability of 0.47.
6 Fuzzy is Economical Zadeh: Make use of the leeway of fuzziness. Fuzziness as a principle of economics: Precision is expensive. Only apply as much precision to a problem as necessary. Example: Backing into a parking space How long would it take if we had to park a car with a precision of ±2 mm?
7 Fuzzy Systems 1. Motivation 2. Fuzzy Sets 3. Fuzzy Numbers 4. Fuzzy Sets and Fuzzy Rules 5. Extracting Fuzzy Models from Data 6. Examples of Fuzzy Systems
8 Basics of Fuzzy Sets Example: the set of young people young = {x P age(x) 20} We can define a characteristic function for this set: { 1 : age(x) 20 µ young (x) = 0 : 20 < age(x)
9 Basics of Fuzzy Sets Fuzzy set theory offers a variable notion of membership: A person of age 25 could still belong to the set of young people, but only to a degree of less than on, say : age(x) 20 µ young (x) = 1 age(x) : 20 < age(x) 30 0 : 30 < age(x) Now the set of young contains people with ages between 20 and 30, with a linearly decreasing degree of membership.
10 Fuzzy Membership Function µ young (x) = 1 : age(x) 20 1 age(x) : 20 < age(x) 30 0 : 30 < age(x)
11 Shapes for Membership Fcts. Triangle: [a,b,c] Trapezoid: [a,b,c,d] Singleton: [a,m] Gaussian: [a,q]
12 Parameters of FMFs Support: s A := x : µ A x > 0 The area where the membership function is positive. Core: c A := x : µ A x = 1 The area for which elements have a maximum degree of membership to the fuzzy set A.
13 Parameters of FMFs α-cut: α A := x : µ A x = α The cut through the membership function of A at height a. Height: h A := max x µ A x The maximum value of the membership function of A.
14 Support: s A := x : µ A x > 0 Triangle: [a,b,c] Trapezoid: [a,b,c,d] Singleton: [a,m] Gaussian: [a,q]
15 Core: c A := x : µ A x = 1 Triangle: [a,b,c] Trapezoid: [a,b,c,d] m=1 Singleton: [a,m] Gaussian: [a,q]
16 α Cut: α A := x : µ A x = α a a Triangle: [a,b,c] Trapezoid: [a,b,c,d] a a Singleton: [a,m] Gaussian: [a,q]
17 Height: h A := max x µ A x Height = 1 Height = 1 Triangle: [a,b,c] Trapezoid: [a,b,c,d] Height = 1 Height = m Singleton: [a,m] Gaussian: [a,q]
18 Linguistic Variables The covering of a variable domain with several fuzzy sets, together with a corresponding semantics, defines a linguistic variable. Example: linguistic variable ag
19 Granulation Using fuzzy sets, we can incorporate the fact that no sharp boundaries between groups, such as young, middle aged, and old, exist. The corresponding membership functions overlap in certain areas, forming non crisp fuzzy boundaries. This compositional way of defining fuzzy sets over a domain of a variable is called granulation.
20 Fuzzy Granules Granulation results in a grouping of objects into imprecise clusters of fuzzy granules. The objects forming a granule are drawn together by similarity. This can be seen as a form of fuzzy data compression.
21 Finding Fuzzy Granules If expert knowledge on a domain is not available, an automatic granulation is used. Standard granulation using an odd number of membership functions: NL: negative large, NM: negative medium, NS: negative small, Z: zero,...
22 Fuzzy Systems 1. Motivation 2. Fuzzy Sets 3. Fuzzy Numbers 4. Fuzzy Sets and Fuzzy Rules 5. Extracting Fuzzy Models from Data 6. Examples of Fuzzy Systems
23 Fuzzy vs. Crisp Numbers Real world measurements are always imprecise. Usually, such imprecise measurements are modeled through a crisp number x, denoting the most typical value, together with an interval, describing the amount of imprecision. In a linguistic sense: about x
24 Fuzzy Numbers as Fuzzy Sets Fuzzy numbers are a special type of fuzzy sets with specific membership functions: µ A must be normalized c A. µ A must be singular. There is precisely one point which lies inside the core, modeling the typical value = modal value of the fuzzy number. µ A must be monotonically increasing left of the core and monotonically decreasing on the right only one peak!.
25 Fuzzy Number Example Typically, triangular membership functions are chosen for fuzzy numbers. About 2 About About 0
26 Addition: Crisp Numbers µ a+b (x) = { 1 : i f y,z R : µa (y) = 1 µ b (z) = 1 y + z = x 0 : else
27 Operations on Fuzzy Numbers We extend classical operators addition, multiplication, etc. to their fuzzy counterparts, such that we can also handle intermediate degrees of membership. For an arbitrary binary operator : µ A B (x) = max y,z {min{µ A (y),µ B (z)} y z = x}
28 Fuzzy Addition: Crisp Numbers We check whether the fuzzy addition is consistent with normal addition on crisp numbers. µ A B (x) = max y,z {min{µ A (y),µ B (z)} y z = x}
29 Fuzzy Addition µ A B (x) = max y,z {min{µ A (y),µ B (z)} y z = x}
30 Fuzzy Addition µ A B (x) = max y,z {min{µ A (y),µ B (z)} y z = x}
31 Fuzzy Addition & Multiplication µ A B (x) = max y,z {min{µ A (y),µ B (z)} y z = x}
32 Fuzzy Addition & Multiplication m looses its triangular shape! µ A B (x) = max y,z {min{µ A (y),µ B (z)} y z = x}
33 Fuzzy Number Operations For practical purposes, we can calculate the result of applying a monotonical operation on fuzzy numbers as follows: Subdivide µ A x and µ B x into monotonically increasing and decreasing parts. Perform the operation jointly on the increasing decreasing parts of numbers A and B. Plateaus can be dealt with in a single computation step.
34 Fuzzy Number Operations Let A and B be fuzzy numbers, and a strongly monotonical operation. Let a 1, a 2 and b 1, b 2 be the intervals in which µ A x and µ B x are monotonically increasing decreasing. If there exist subintervals α 1, α 2 a 1, a 2 and β 1, β 2 b 1, b 2, such that x A 1, 2, x B 1, 2 : A x A = B x B = then t 1 1, 2 2 : A B t =
35 Fuzzy Addition m A + B (t) = 1.0 t =...
36 Fuzzy Addition m A + B (t) = 1.0 t = = 110
37 Fuzzy Addition m A + B (t) = 0.4 " t [...,...]
38 Fuzzy Addition m A + B (t) = 0.4 " t [20+64, 30+64]
39 Fuzzy Addition m A + B (t) = 0.4 " t [20+64, 30+64] = [84, 94]
40 Fuzzy Addition m A + B (t) = 0.4 t =...
41 Fuzzy Addition m A + B (t) = 0.4 t = = 122
42 Fuzzy Addition m A + B (t) = 0.4 t = = 122
43 Fuzzy Addition Support: s A + B = [...,...]
44 Fuzzy Addition Support: s A + B = [10+60, 50+80] = [70, 130]
45 Fuzzy Addition and the rest by linear interpolation: Done!
46 Monotonic Function µ f (A) (y) = max{µ A (x) x : f (x) = y}
47 Fuzzy Systems 1. Motivation 2. Fuzzy Sets 3. Fuzzy Numbers 4. Fuzzy Sets and Fuzzy Rules 5. Extracting Fuzzy Models from Data 6. Examples of Fuzzy Systems
48 Operations on Fuzzy Sets Let A and B be fuzzy sets. Intersection conjunction : µ A B x = min A x, B x Union disjunction : µ A B x = max A x, B x Complement: µ A x = 1 A x
49 Fuzzy Union & Intersection µ A B x = max A x, B x µ A B x = min A x, B x
50 Fuzzy Union & Intersection
51 Union & Intersection: Alternatives µ A B (x) = min{µ A (x),µ B (x)} µ A B (x) = max{µ A (x),µ B (x)} µ A B (x) = µ A (x) µ B (x) µ A B (x) = min{µ A (x) + µ B (x),1}
52 Fuzzy Rules IF temperature = low THEN cooling valve = half open. IF temperature = medium THEN cooling valve = almost open.
53 Max Min Inference Max-Min Inference temperature = low valve = half open Approximate Reasoning IF temperature = low THEN cooling valve = half open temperature = medium valve = almost open IF temperature = medium THEN cooling valve = almost open in out
54 Max Min Inference Max-Min Inference temperature = low valve = half open Approximate Reasoning IF temperature = low THEN cooling valve = half open temperature = medium valve = almost open 1. Input of crisp value IF temperature = medium THEN cooling valve = almost open in out
55 Max Min Inference Max-Min Inference temperature = low valve = half open Approximate Reasoning IF temperature = low THEN cooling valve = half open temperature = medium valve = almost open 1. Input of crisp value 2. Fuzzification IF temperature = medium THEN cooling valve = almost open in out
56 Max Min Inference Max-Min Inference temperature = low valve = half open Approximate Reasoning IF temperature = low THEN cooling valve = half open temperature = medium valve = almost open 1. Input of crisp value 2. Fuzzification 3. Inference IF temperature = medium THEN cooling valve = almost open in out
57 Max Min Inference Max-Min Inference temperature = low valve = half open Approximate Reasoning IF temperature = low THEN cooling valve = half open temperature = medium valve = almost open 1. Input of crisp value 2. Fuzzification IF temperature = medium THEN cooling valve = almost open 3. Inference 4. Output set Fuzzy OR in out
58 Max Min Inference Max-Min Inference temperature = low valve = half open Approximate Reasoning IF temperature = low THEN cooling valve = half open temperature = medium valve = almost open 1. Input of crisp value 2. Fuzzification IF temperature = medium THEN cooling valve = almost open 3. Inference 4. Output set Fuzzy OR in out
59 Max Min Inference Max-Min Inference temperature = low valve = half open Approximate Reasoning IF temperature = low THEN cooling valve = half open temperature = medium valve = almost open 1. Input of crisp value 2. Fuzzification 3. Inference 4. Output set 5. Defuzzification IF temperature = medium THEN cooling valve = almost open Center of gravity in out
60 Fuzzy Systems 1. Motivation 2. Fuzzy Sets 3. Fuzzy Numbers 4. Fuzzy Sets and Fuzzy Rules 5. Extracting Fuzzy Models from Data 6. Examples of Fuzzy Systems
61 Extracting Fuzzy Models: Graphs Approximate representation of functions, contours, and relations
62 Global Granulation of Input Space
63 Fixed Grid Rules Extraction
64 Adaptive Grid Rules Extraction
65 Fuzzy Systems 1. Motivation 2. Fuzzy Sets 3. Fuzzy Numbers 4. Fuzzy Sets and Fuzzy Rules 5. Extracting Fuzzy Models from Data 6. Examples of Fuzzy Systems
66 Logic System A logic system consists of four parts: Alphabet: a set of basic symbols from which more complex sentences are made. Syntax: a set of rules or operators for constructing expressions sentences. Semantics: for defining the meaning of the sentences Inference rules: for constructing semantically equivalent but syntactically different sentences
67 Predicate Logic The following types of symbols are allowed in predicate logic: Terms Predicates Connectives Quantifiers
68 Predicate Logic Terms: Constant symbols: symbols, expressions, or entities which do not change during excecution e.g., true / false Variable symbols: represent entities that can change during excecution Function symbols: represent functions which process input values on a predefined list of parameters and obtain resulting values
69 Predicate Logic Predicates: Predicate symbols: represent true/false type relations between objects. Objects are represented by constant symbols.
70 Predicate Logic Connectives: Conjunction Disjunction Negation Implication Equivalence... same as for propositional calculus
71 Predicate Logic Quantifiers: valid for variable symbols Existential quantifier: There exists at least one value for x from its domain. Universal quantifier: For all x in its domain.
72 First Order Logic First order logic allows quantified variables to refer to objects, but not to predicates or functions. For applying an inference to a set of predicate expressions, the system has to process matches of expressions. The process of matching is called unification.
73 PROLOG Programming in Logic In PROLOG, a quantifier free Horn clausal notation is adopted. "x, Human(x) Mortal(x) mortal(x) :- human(x). Human(Socrates) human(socrates).
74 PROLOG: Horn Clauses A rule A1, A2,..., An B as a Horn clause has the following form: B :- A1, A2,..., An. The goal B is true if all the sub goals A1, A2,..., An are true. A1, A2,..., An are predicate expressions.
75 PROLOG: Facts A fact is represented as a literal clause with the right side being the constant true. Therefore, a clause representing a fact is always true. Example: human(socrates) :- true. human(socrates).
76 Knowledge Representation In PROLOG knowledge is represented as a set of clauses with premises right side of the clause and one conclusion left side. AND is denoted by the character,. OR is denoted by the character ;. NOT is denoted by.
77 PROLOG Architecture
78 Family Example: Facts father(john, Mary). father(jack, Andy). father(jack, Tom). mother(helen, Jack). mother(mary, Jilly). grandfather(barry, Jim).
79 Family Example: Rules grandfather(x,y) :- father(x,z), parent(z,y). grandmother(x,y) :- mother(x,z), parent(z,y). parent(x,y) :- mother(x,y). parent(x,y) :- father(x,y).
80 PROLOG Sample Dialogue Goal?- grandfather(john, Jilly). yes Goal?- grandmother(helen, X). 2 solutions X = Andy; X = Tom Goal?- grandmother(x,y), X = Y. fail
81 PROLOG Sample Inference grandfather(john, Jilly) AND father(john, Z) parent(z, Jilly) Mary father(john, Mary). father(jack, Andy). father(jack, Tom). mother(helen, Jack). mother(mary, Jilly). grandfather(barry, Jim). mother(z, Jilly) Mary OR father(z, Jilly) Goal? grandfather(john, Jilly). yes
82 PROLOG Sample Inference grandmother(helen, X) AND mother(helen, Z) parent(z, X) Jack father(john, Mary). father(jack, Andy). father(jack, Tom). mother(helen, Jack). mother(mary, Jilly). grandfather(barry, Jim). OR mother(z, X) father(z, X) Goal? grandmother(helen, X). 2 solutions: X= Andy or X= Tom Jack Andy Tom
83 References Berthold, M., and Hand, D. J Intelligent Data Analysis. Berlin, Springer. Bothe, H Fuzzy Logic Einführung in die Theori und Anwendungen. Berlin, Springer. Kasabov, N Foundations of Neural Networks, Fuzzy Systems, amd Knowledge Engineering. Cambridge, MA, MIT Press.
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