aleksander gegov
TRANSCRIPT
2
Presentation Outline
Part 1 - Theory: Introduction
Types of Fuzzy Systems
Formal Models for Fuzzy Networks
Basic Operations in Fuzzy Networks
Structural Properties of Basic Operations
Advanced Operations in Fuzzy Networks
Part 2 - Applications: Feedforward Fuzzy Networks
Feedback Fuzzy Networks
Evaluation of Fuzzy Networks
Fuzzy Network Toolbox
Conclusion
References
3
Introduction
Modelling Aspects of Systemic Complexity
• non-linearity (input-output functional relationships)
• uncertainty (incomplete and imprecise data)
• dimensionality (large number of inputs and outputs)
• structure (interacting subsystems)
Complexity Management by Fuzzy Systems
• model feasibility (achievable in the case of non-linearity)
• model accuracy (achievable in the case of uncertainty)
• model efficiency (problematic in the case of dimensionality)
• model transparency (problematic in the case of structure)
4
Types of Fuzzy Systems
Fuzzification
• triangular membership functions
• trapezoidal membership functions
Inference
• truncation of rule membership functions by firing strengths
• scaling of rule membership functions by firing strengths
Defuzzification
• centre of area of rule base membership function
• weighted average of rule base membership function
5
Types of Fuzzy Systems
Logical Connections
• disjunctive antecedents and conjunctive rules
• conjunctive antecedents and conjunctive rules
• disjunctive antecedents and disjunctive rules
• conjunctive antecedents and disjunctive rules
Inputs and Outputs
• single-input-single-output
• single-input-multiple-output
• multiple-input-single-output
• multiple-input-multiple-output
6
Types of Fuzzy Systems
Operation Mode
• Mamdani (conventional fuzzy system)
• Sugeno (modern fuzzy system)
• Tsukamoto (hybrid fuzzy system)
Rule Base
• single rule base (standard fuzzy system)
• multiple rule bases (hierarchical fuzzy system)
• networked rule bases (fuzzy network)
7
Types of Fuzzy Systems
Figure 1: Standard fuzzy system
8
Types of Fuzzy Systems
Figure 2: Hierarchical fuzzy system
9
Types of Fuzzy Systems
Figure 3: Fuzzy network
10
Formal Models for Fuzzy Networks
Node Modelling by If-then Rules
Rule 1: If x is low, then y is small
Rule 2: If x is average, then y is medium
Rule 3: If x is high, then y is big
Node Modelling by Integer Tables
Linguistic terms for x Linguistic terms for y
1 (low) 1 (small)
2 (average) 2 (medium)
3 (high) 3 (big)
11
Formal Models for Fuzzy Networks
Node Modelling by Boolean Matrices
x / y 1 (small) 2 (medium) 3 (big)
1 (low) 1 0 0
2 (average) 0 1 0
3 (high) 0 0 1
Node Modelling by Binary Relations
{(1, 1), (2, 2), (3, 3)}
12
Formal Models for Fuzzy Networks
Network Modelling by Grid Structures
Layer 1
Level 1 N11(x, y)
Network Modelling by Interconnection Structures
Layer 1
Level 1 y
13
Formal Models for Fuzzy Networks
Network Modelling by Block Schemes
x N11 y
Network Modelling by Topological Expressions
[N11] (x | y)
14
Basic Operations in Fuzzy Networks
Horizontal Merging of Nodes
[N11] (x11 | z11,12) * [N12] (z11,12
| y12) = [N11*12] (x11 | y12)
* symbol for horizontal merging
Horizontal Splitting of Nodes
[N11/12] (x11 | y12) = [N11] (x11
| z11,12) / [N12] (z11,12 | y12)
/ symbol for horizontal splitting
15
Basic Operations in Fuzzy Networks
Vertical Merging of Nodes
[N11] (x11 | y11) + [N21] (x21
| y21) = [N11+21] (x11, x21 | y11, y21)
+ symbol for vertical merging
Vertical Splitting of Nodes
[N11–21] (x11, x21 | y11, y21) = [N11] (x11
| y11) – [N21] (x21 | y21)
– symbol for vertical splitting
16
Basic Operations in Fuzzy Networks
Output Merging of Nodes
[N11] (x11,21 | y11) ; [N21] (x11,21
| y21) = [N11;21] (x11,21 | y11, y21)
; symbol for output merging
Output Splitting of Nodes
[N11:21] (x11, 21 | y11, y21) = [N11] (x11,21
| y11) : [N21] (x11,21 | y21)
: symbol for output splitting
17
Structural Properties of Basic Operations
Associativity of Horizontal Merging
[N11] (x11 | z11,12) * [N12] (z11,12
| z12,13) * [N13] (z12,13 | y13) =
[N11*12] (x11 | z12,13) * [N13] (z12,13
| y13) =
[N11] (x11 | z11,12) * [N12*13] (z11,12
| y13) =
[N11*12*13] (x11 | y13)
18
Structural Properties of Basic Operations
Variability of Horizontal Splitting
[N11/12/13] (x11 | y13) =
[N11] (x11 | z11,12) / [N12/13] (z11,12
| y13) =
[N11/12] (x11 | z12,13) / [N13] (z12,13
| y13) =
[N11] (x11 | z11,12) / [N12] (z11,12
| z12,13) / [N13] (z12,13 | y13)
19
Structural Properties of Basic Operations
Associativity of Vertical Merging
[N11] (x11 | y11) + [N21] (x21
| y21) + [N31] (x31 | y31) =
[N11+21] (x11, x21 | y11,
y21) + [N31] (x31 | y31) =
[N11] (x11 | y11) + [N21+31] (x21,
x31 | y21, y31) =
[N11+21+31] (x11, x21,
x31 | y11, y21,
y31)
20
Structural Properties of Basic Operations
Variability of Vertical Splitting
[N11–21–31] (x11, x21,
x31 | y11, y21,
y31) =
[N11] (x11 | y11) – [N21–31] (x21,
x31 | y21, y31) =
[N11–21] (x11, x21 | y11,
y21) – [N31] (x31 | y31) =
[N11] (x11 | y11) – [N21] (x21
| y21) – [N31] (x31 | y31)
21
Structural Properties of Basic Operations
Associativity of Output Merging
[N11] (x11,21,31 | y11) ; [N21] (x11,21,31
| y21) ; [N31] (x11,21,31 | y31) =
[N11;21] (x11,21,31 | y11, y21) ; [N31] (x11,21,31
| y31) =
[N11] (x11,21,31 | y11) ; [N21;31] (x11,21,31 | y21,
y31) =
[N11;21;31] (x11,21,31 | y11, y21,
y31)
22
Structural Properties of Basic Operations
Variability of Output Splitting
[N11:21:31] (x11,21,31 | y11, y21,
y31) =
[N11] (x11,21,31 | y11) : [N21:31] (x11,21,31 | y21,
y31) =
[N11:21] (x11,21,31 | y11, y21) : [N31] (x11,21,31
| y31) =
[N11] (x11,21,31 | y11) : [N21] (x11,21,31
| y21) : [N31] (x11,21,31 | y31)
23
Advanced Operations in Fuzzy Networks
Node Transformation in Input Augmentation
[N] (x | y) ⇒ [NAI] (x , xAI | y)
Node Transformation in Output Permutation
[N] (x | y1, y2) ⇒ [NPO] (x | y2, y1)
Node Transformation in Feedback Equivalence
[N] (x, z | y, z) ⇒ [NEF] (x, xEF | y, yEF)
24
Advanced Operations in Fuzzy Networks
Node Identification in Horizontal Merging
A * U = C
A, C – known nodes, U – non-unique unknown node
U * B = C
B, C – known nodes, U – non-unique unknown node
25
Advanced Operations in Fuzzy Networks
A * U * B = C
A, B, C – known nodes, U – non-unique unknown node
A * U = D
D * B = C
D – non-unique unknown node
U * B = E
A * E = C
E – non-unique unknown node
26
Advanced Operations in Fuzzy Networks
Node Identification in Vertical Merging
A + U = C
A, C – known nodes, U – unique unknown node
U + B = C
B, C – known nodes, U – unique unknown node
27
Advanced Operations in Fuzzy Networks
A + U + B = C
A, B, C – known nodes, U – unique unknown node
A + U = D
D + B = C
D – unique unknown node
U + B = E
A + E = C
E – unique unknown node
28
Advanced Operations in Fuzzy Networks
Node Identification in Output Merging
A ; U = C
A, C – known nodes, U – unique unknown node
U ; B = C
B, C – known nodes, U – unique unknown node
29
Advanced Operations in Fuzzy Networks
A ; U ; B = C
A, B, C – known nodes, U – unique unknown node
A ; U = D
D ; B = C
D – unique unknown node
U ; B = E
A ; E = C
E – unique unknown node
30
Feedforward Fuzzy Networks
Network with Single Level and Single Layer
• fuzzy system
1,1
[N11] (x11 | y11)
31
Feedforward Fuzzy Networks
Network with Single Level and Multiple Layers
• queue of ‘n’ fuzzy systems
1,1 … 1,n
32
Feedforward Fuzzy Networks
Network with Multiple Levels and Single Layer
• stack of ‘m’ fuzzy systems
1,1
…
m,1
33
Feedforward Fuzzy Networks
Network with Multiple Levels and Multiple Layers
• grid of ‘m by n’ fuzzy systems
1,1 … 1,n
… … …
m,1 … m,n
34
Feedback Fuzzy Networks
Network with Single Local Feedback
• one node embraced by feedback
N(F) N
N N
N(F) – node embraced by feedback
N – nodes with no feedback
N N(F)
N N
N N
N(F) N N N
N N(F)
35
Feedback Fuzzy Networks
Network with Multiple Local Feedback
• at least two nodes embraced by separate feedback
N(F1) N(F2)
N N
N(F1), N(F2) – nodes embraced by separate feedback
N – nodes with no feedback
N N
N(F1) N(F2)
N(F1) N
N N(F2)
N(F1) N
N(F2) N
N N(F1)
N N(F2)
N N(F1)
N(F2) N
36
Feedback Fuzzy Networks
Network with Single Global Feedback
• one set of at least two adjacent nodes embraced by feedback
N1(F) N2(F)
N N
N1(F) N
N2(F) N
{N1(F), N2(F)} – set of nodes embraced by feedback
N – nodes with no feedback
N N
N1(F) N2(F)
N N1(F)
N N2(F)
37
Feedback Fuzzy Networks
Network with Multiple Global Feedback
• at least two sets of nodes embraced by separate feedback with
at least two adjacent nodes in each set
N1(F1) N2(F1)
N1(F2) N2(F2)
{N1(F1), N2(F1)}, {N1(F2), N2(F2)} – sets of nodes embraced
by separate feedback
N1(F1) N1(F2)
N2(F1) N2(F2)
38
Evaluation of Fuzzy Networks
Linguistic Evaluation Metrics
• composition of hierarchical into standard fuzzy systems
• decomposition of standard into hierarchical fuzzy systems
Composition Formula
NE,x = *p=1x–1 (N1,p + +q=p+1
x–1 Iq,p)
NE,x – equivalent node for a fuzzy network with x inputs
39
Evaluation of Fuzzy Networks
Decomposition Algorithm
1. Find N1,1 from the first two inputs and the output.
2. If x=2, go to end.
3. Set k=3.
4. While k≤x, do steps 5-7.
5. Find NE,k from the first k inputs and the output.
6. Derive N1,k–1 from the formula for NE,k, if possible.
7. Set k=k+1.
8. Endwhile.
NE,k – equivalent node for a fuzzy subnetwork with first k inputs
40
Evaluation of Fuzzy Networks
Functional Evaluation Metrics
• model performance indicators
• applications to case studies
Feasibility Index (FI)
FI = sum i=1n (pi / n)
n – number of non-identity nodes
p i – number of inputs to the i-th non-identity node
lower FI ⇒ better feasibility
41
Evaluation of Fuzzy Networks
Accuracy Index (AI)
AI = sum i=1nl sum j=1
qil sum k=1vji (|yji
k – djik| / vij)
nl – number of nodes in the last layer
qil – number of outputs from the i-th node in the last layer
vji – number of discrete values for the j-th output from the i-th
node in the last layer
yjik , dji
k – simulated and measured k-th discrete value for the j-th
output from the i-th node in the last layer
lower AI ⇒ better accuracy
42
Evaluation of Fuzzy Networks
Efficiency Index (EI)
EI = sum i=1n (qi
FID . riFID)
n – number of non-identity nodes
FID – fuzzification-inference-defuzzification
qiFID
– number of outputs from the i-th non-identity node with an
associated FID sequence
riFID
– number of rules for the i-th non-identity node with an
associated FID sequence
lower EI ⇒ better efficiency
43
Evaluation of Fuzzy Networks
Transparency Index (TI)
TI = (p + q) / (n + m)
p – overall number of inputs
q – overall number of outputs
n – number of non-identity nodes
m – number of non-identity connections
lower TI ⇒ better transparency
44
Evaluation of Fuzzy Networks
Case Study 1 (Ore Flotation)
Inputs:
x1 – copper concentration in ore pulp
x2 – iron concentration in ore pulp
x3 – debit of ore pulp
Output:
y – new copper concentration in ore pulp
45
Evaluation of Fuzzy Networks
Figure 4: Standard fuzzy system (SFS) for case study 1
46
Evaluation of Fuzzy Networks
Figure 5: Hierarchical fuzzy system (HFS) for case study 1
47
Evaluation of Fuzzy Networks
Figure 6: Fuzzy network (FN) for case study 1
48
Evaluation of Fuzzy Networks
Table 1: Models performance for case study 1
Index / Model Standard
Fuzzy System
Hierarchical
Fuzzy System
Fuzzy
Network
Feasibility 3 2 2
Accuracy 4.35 4.76 4.60
Efficiency 343 126 343
Transparency 4 1.33 1.33
FI – FN better than SFS and equal to HFS
AI – FN worse than SFS and better than HFS
EI – FN equal to SFS and worse than HFS
TI – FN better than SFS and equal to HFS
49
Evaluation of Fuzzy Networks
Case Study 2 (Retail Pricing)
Inputs:
x1 – expected selling price for retail product
x2 – difference between selling price and cost for retail product
x3 – expected percentage to be sold from retail product
Output:
y – maximum cost for retail product
50
Evaluation of Fuzzy Networks
0 20 40 60 80 100 120 1400
10
20
30
40
50
60
70
80
90
100
input permutations
outp
ut
Figure 7: Standard fuzzy system (SFS) for case study 2
51
Evaluation of Fuzzy Networks
0 20 40 60 80 100 120 1400
20
40
60
80
100
input permutations
ou
tpu
t
Figure 8: Hierarchical fuzzy system (HFS) for case study 2
52
Evaluation of Fuzzy Networks
0 20 40 60 80 100 120 1400
20
40
60
80
100
input permutations
outp
ut
Figure 9: Fuzzy network (FN) for case study 2
53
Evaluation of Fuzzy Networks
Table 2: Models performance for case study 2
Index / Model Standard
Fuzzy System
Hierarchical
Fuzzy System
Fuzzy
Network
Feasibility 3 2 2
Accuracy 2.86 5.57 3.64
Efficiency 125 80 125
Transparency 4 1.33 1.33
FI – FN better than SFS and equal to HFS
AI – FN worse than SFS and better than HFS
EI – FN equal to SFS and worse than HFS
TI – FN better than SFS and equal to HFS
54
Evaluation of Fuzzy Networks
Composition of Hierarchical into Standard Fuzzy System
• full preservation of model feasibility
• maximal improvement of model accuracy
• fixed loss of model efficiency
• full preservation of model transparency
Decomposition of Standard into Hierarchical Fuzzy System
• no change of model feasibility
• minimal loss of model accuracy
• fixed improvement of model efficiency
• no change of model transparency
55
Fuzzy Network Toolbox
Toolbox Details
• developed by the PhD student Nedyalko Petrov
• implemented in the Matlab environment
• to be uploaded on the Mathworks web site
• to be uploaded on the Springer web site
Toolbox Structure
• Matlab files for basic operations on nodes
• Matlab files for advanced operations on nodes
• Matlab files for auxiliary operations
• Word files with illustration examples
56
Conclusion
Theoretical Significance of Fuzzy Networks
• novel application of discrete mathematics and control theory
• detailed validation by test examples and case studies
Methodological Impact of Fuzzy Networks
• extension of standard and hierarchical fuzzy systems
• bridge between standard and hierarchical fuzzy systems
• novel framework for non-fuzzy rule based systems
Application Areas of Fuzzy Networks
• modelling and simulation in the mining industry
• modelling and simulation in the retail industry
57
Conclusion
Other Applications of Fuzzy Networks
• finance (mortgage evaluation)
• healthcare (radiotherapy margins)
• robotics (path planning)
• games (obstacle avoidance)
• sports (boxer training)
• security (terrorist threat)
• environment (natural preservation)
58
References
A.Gegov, Complexity Management in Fuzzy Systems,
Springer, Berlin, 2007
A.Gegov, Fuzzy Networks for Complex Systems,
Springer, Berlin, 2010