fuzzy sets neuro-fuzzy and soft computing: fuzzy sets
DESCRIPTION
Fuzzy Sets: Outline Introduction Basic definitions and terminology 2017/4/27 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 Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.TRANSCRIPT
Fuzzy Sets
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
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Fuzzy Sets: Outline
IntroductionBasic definitions and terminologySet-theoretic operationsMF 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
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
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Fuzzy Sets
Sets with fuzzy boundaries
A = Set of tall people
Heights5’10’’
1.0
Crisp set A
Membershipfunction
Heights5’10’’ 6’2’’
.5
.9
Fuzzy set A1.0
Neuro-Fuzzy and Soft Computing: Fuzzy Sets
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Membership Functions (MFs)
Characteristics of MFs:• Subjective measures• Not probability functions
MFs
Heights5’10’’
.5
.8
.1
“tall” in Asia
“tall” in the US
“tall” in NBA
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Fuzzy Sets
Formal definition:A fuzzy set A in X is expressed as a set of ordered
pairs:
A x x x XA {( , ( ))| }
Universe oruniverse of discourse
Fuzzy set Membershipfunction
(MF)
A fuzzy set is totally characterized by aA fuzzy set is totally characterized by amembership function (MF).membership function (MF).
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Fuzzy Sets with Discrete UniversesFuzzy 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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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 x( )
1
1 5010
2
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Alternative Notation
A fuzzy set A can be alternatively denoted as follows:
A x xAx X
i ii
( ) /
A x xAX
( ) /
X is discrete
X is continuous
Note that and integral signs stand for the union of membership grades; “/” stands for a marker and does not imply division.
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Fuzzy Partition
Fuzzy partitions formed by the linguistic values “young”, “middle aged”, and “old”:
lingmf.m
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Set-Theoretic Operations
Subset:
Complement:
Union:
Intersection:
A B A B
C A B x x x x xc A B A B ( ) max( ( ), ( )) ( ) ( )
C A B x x x x xc A B A B ( ) min( ( ), ( )) ( ) ( )
A X A x xA A ( ) ( )1
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Set-Theoretic Operations
subset.m
fuzsetop.m
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MF Formulation
Triangular MF: trimf x a b cx ab a
c xc b( ; , , ) max min , ,
0
Trapezoidal MF: trapmf x a b c dx ab a
d xd c( ; , , , ) max min , , ,
1 0
Generalized bell MF: gbellmf x a b cx cb
b( ; , , )
1
12
Gaussian MF: gaussmf x a b c ex c
( ; , , )
12
2
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MF Formulation
disp_mf.m
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MF Formulation
Sigmoidal MF: sigmf x a b c e a x c( ; , , ) ( )
11
Extensions:
Abs. differenceof two sig. MF
Productof two sig. MF
disp_sig.m
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MF FormulationL-R MF:
LR x cF
c xx c
Fx c
x c
L
R
( ; , , ),
,
Example: F x xL ( ) max( , ) 0 1 2 F x xR ( ) exp( ) 3
difflr.m
c=65a=60b=10
c=25a=10b=40
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Mamdani Fuzzy Models
Graphics representation:A1 B1
A2 B2
T-norm
X
X
Y
Y
w1
w2
C1
C2
Z
Z
C’Z
X Yx is 4.5 y is 56.8 z is zCOA