Transcript Slides for Fuzzy Sets, Ch. 2 of Neuro

Neuro-Fuzzy and Soft Computing: Fuzzy Sets
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 Sets
Fuzzy Sets: Outline
Introduction
Basic definitions and terminology
Set-theoretic operations
MF formulation and parameterization
• MFs of one and two dimensions
• Derivatives of parameterized MFs
More on fuzzy union, intersection, and complement
• Fuzzy complement
• Fuzzy intersection and union
• Parameterized T-norm and T-conorm
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Sets
Sets with fuzzy boundaries
A = Set of tall people
Crisp set A
1.0
Fuzzy set A
1.0
.9
Membership
.5
function
5’10’’
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Heights
5’10’’ 6’2’’
Heights
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Membership Functions (MFs)
Characteristics of MFs:
• Subjective measures
• Not probability functions
“tall” in Asia
MFs
.8
“tall” in the US
.5
“tall” in NBA
.1
5’10’’
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Heights
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Sets
Formal definition:
A fuzzy set A in X is expressed as a set of ordered
pairs:
A  {( x ,  A ( x ))| x  X }
Fuzzy set
Membership
function
(MF)
Universe or
universe of discourse
A fuzzy set is totally characterized by a
membership function (MF).
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Sets with Discrete Universes
Fuzzy set C = “desirable city to live in”
X = {SF, Boston, LA} (discrete and nonordered)
C = {(SF, 0.9), (Boston, 0.8), (LA, 0.6)}
Fuzzy set A = “sensible number of children”
X = {0, 1, 2, 3, 4, 5, 6} (discrete universe)
A = {(0, .1), (1, .3), (2, .7), (3, 1), (4, .6), (5, .2), (6, .1)}
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Sets with Cont. Universes
Fuzzy set B = “about 50 years old”
X = Set of positive real numbers (continuous)
B = {(x, B(x)) | x in X}
B(x) 
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1
 x  50 
1 

 10 
2
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Alternative Notation
A fuzzy set A can be alternatively denoted as
follows:
X is discrete
X is continuous
A

A
( xi ) / xi
xi X
A    A( x) / x
X
Note that S and integral signs stand for the union of
membership grades; “/” stands for a marker and does
not imply division.
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Partition
Fuzzy partitions formed by the linguistic values
“young”, “middle aged”, and “old”:
lingmf.m
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
More Definitions
Support
Core
Normality
Crossover points
Fuzzy singleton
a-cut, strong a-cut
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Convexity
Fuzzy numbers
Bandwidth
Symmetricity
Open left or right, closed
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
MF Terminology
MF
1
.5
a
0
Core
Crossover points
a - cut
Support
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X
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Normality and Fuzzy Singleton
A fuzzy set A is Normal, If its core is nonempty.
Fuzzy singleton: a normal fuzzy set whose
support has only one member.
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Convexity of Fuzzy Sets
A fuzzy set A is convex iff for any l in [0, 1],
A ( lx1  ( 1  l ) x 2 )  min( A ( x1 ), A ( x 2 ))
Alternatively, A is convex is all its a-cuts are
convex.
convexmf.m
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Set-Theoretic Operations
Subset:
A  B  A  B
Complement:
A  X  A  A ( x )  1  A ( x )
Union:
C  A  B  c ( x )  max( A ( x ), B ( x ))  A ( x ) B ( x )
Intersection:
C  A  B  c ( x )  min( A ( x ), B ( x ))  A ( x ) B ( x )
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Set-Theoretic Operations
subset.m
fuzsetop.m
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
MF Formulation

Triangular MF:
Trapezoidal MF:

d  x 
 x a
trapmf ( x ; a , b , c , d )  max min
, 1,
 , 0
b a
d c  


Gaussian MF:
Generalized bell MF:
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x a c  x 
,
 , 0
b a c b 
trimf ( x ; a , b , c )  max min
gaussmf ( x;  c)  e
gbellmf ( x; a, b, c ) 
1  x c 
 

2  
2
1
xc
1
a
2b
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
MF Formulation
disp_mf.m
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
MF Formulation
Sigmoidal MF:
1
sigmf ( x; a, c) 
1 e
 a ( x c )
Extensions:
Abs. difference
of two sig. MF
Product
of two sig. MF
disp_sig.m
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
MF Formulation
L-R MF:
Example:
 c  x
 F L  a  , x  c

LR ( x ; c , a ,  )  
 F  x  c  , x  c
R

   
F L ( x )  max( 0 , 1  x 2 )
c=65
c=25
a=60
a=10
b=10
b=40
difflr.m
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3
FR ( x )  exp( x )
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Complement
General requirements:
• Boundary: N(0)=1 and N(1) = 0
• Monotonicity: N(a) >= N(b) if a= < b
• Involution: N(N(a) = a (optional)
Two types of fuzzy complements:
• Sugeno’s complement:
1 a
N (a) 
, s  
1  sa
s
• Yager’s complement:
N w (a)  (1  a w )1/ w
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w0
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Complement
Sugeno’s complement:
1 a
N s (a ) 
1  sa
Yager’s complement:
N w ( a )  (1  a w )1/ w
negation.m
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Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Intersection: T-norm
Basic requirements:
• Boundary: T(0, a) = 0, T(a, 1) = T(1, a) = a
• Monotonicity: T(a1, b1) =< T(a2, b2) if a1=< a2 and
b1 =< b2
• Commutativity: T(a, b) = T(b, a)
• Associativity: T(a, T(b, c)) = T(T(a, b), c)
Four examples (page 37):
•
•
•
•
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Minimum: Tm(a, b)=min(a,b)
Algebraic product: Ta(a, b)=a*b
Bounded product: Tb(a, b)
Drastic product: Td(a, b)
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
T-norm Operator
Minimum:
Tm(a, b)
Algebraic
product:
Ta(a, b)
tnorm.m
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Bounded
product:
Tb(a, b)
Drastic
product:
Td(a, b)
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Fuzzy Union: T-conorm or S-norm
Basic requirements:
• Boundary: S(1, a) = 1, S(a, 0) = S(0, a) = a
• Monotonicity: S(a1, b1) =< S(a2, b2) if a1 =< a2 and
b1 =< b2
• Commutativity: S(a, b) = S(b, a)
• Associativity: S(a, S(b, c)) = S(S(a, b), c)
Four examples (page 38):
•
•
•
•
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Maximum: Sm(a, b)=Max(a,b)
Algebraic sum: Sa(a, b)=a+b-ab=1-(1-a)(1-b)
Bounded sum: Sb(a, b)
Drastic sum: Sd(a, b)
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
T-conorm or S-norm
Maximum:
Sm(a, b)
Algebraic
sum:
Sa(a, b)
Bounded
sum:
Sb(a, b)
tconorm.m
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Drastic
sum:
Sd(a, b)
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Generalized DeMorgan’s Law
T-norms and T-conorms are duals which
support the generalization of DeMorgan’s law:
• T(a, b) = N(S(N(a), N(b)))
• S(a, b) = N(T(N(a), N(b)))
Tm(a, b)
Ta(a, b)
Tb(a, b)
Td(a, b)
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Sm(a, b)
Sa(a, b)
Sb(a, b)
Sd(a, b)
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Parameterized T-norm and S-norm
Parameterized T-norms and dual T-conorms
have been proposed by several researchers:
•
•
•
•
•
•
•
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Yager
Schweizer and Sklar
Dubois and Prade
Hamacher
Frank
Sugeno
Dombi