Automated Methods for Fuzzy Systems
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1 Automated Methods for Fuzzy Systems Gradient Method Adriano Joaquim de Oliveira Cruz PPGI-UFRJ September 2012 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
2 Summary 1 Introduction Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
3 Summary 1 Introduction 2 Training Standard Fuzzy System Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
4 Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
5 Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
6 Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law 5 Input Membership Function Spreads Update Law Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
7 Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law 5 Input Membership Function Spreads Update Law 6 Example Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
8 Section Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law 5 Input Membership Function Spreads Update Law 6 Example Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
9 A precise model is a contradiction. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
10 Bibliography Kevin M. Passino, Stephen Yurkovich Fuzzy Control in Chapter 5. Addison Wesley Longman, Inc, USA, Timothy J. Ross Fuzzy Logic with Engineering Applications. John Wiley and Sons, Inc, USA, J. R. Jang, C. Sun, E. Mizutani Neuro Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence Prentice Hall, NJ, USA, 1997 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
11 Constructing fuzzy systems How to construct a fuzzy system from numeric data? Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
12 Constructing fuzzy systems How to construct a fuzzy system from numeric data? Using data obtained experimentally from a system, it is possible to identify the model. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
13 Constructing fuzzy systems How to construct a fuzzy system from numeric data? Using data obtained experimentally from a system, it is possible to identify the model. Find a model that fits the data by using fuzzy interpolation capabilities. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
14 Introduction We need to construct a fuzzy system f(x,θ) that approximate the function g represented in the training data G. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
15 Introduction We need to construct a fuzzy system f(x,θ) that approximate the function g represented in the training data G. There is no guarantee that it will succeed. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
16 Introduction We need to construct a fuzzy system f(x,θ) that approximate the function g represented in the training data G. There is no guarantee that it will succeed. It provides a method to tune all parameters of a fuzzy system. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
17 Section Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law 5 Input Membership Function Spreads Update Law 6 Example Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
18 The System Gaussian input membership functions with centers c i j and spreads σ i j. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
19 The System Gaussian input membership functions with centers c i j and spreads σ i j. Output membership function centers b i. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
20 The System Gaussian input membership functions with centers c i j and spreads σ i j. Output membership function centers b i. Product for premise and implication. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
21 The System Gaussian input membership functions with centers cj i and spreads σj i. Output membership function centers b i. Product for premise and implication. Center-average defuzzification. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
22 The System Gaussian input membership functions with centers c i j and spreads σ i j. Output membership function centers b i. Product for premise and implication. Center-average defuzzification. It is described by f(x θ) = [ R i=1 b n i j=1 exp 1 2 ( ) ] 2 x j cj i σj i [ ( ) ] R 2 n i=1 j=1 exp 1 x j cj i 2 σj i Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
23 Error Suppose that you have the m th training data pair (x,y) G. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
24 Error Suppose that you have the m th training data pair (x,y) G. The GM s goal is to minimize the error between the predicted output value, f(x m θ) and the actual output value y m. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
25 Error Suppose that you have the m th training data pair (x,y) G. The GM s goal is to minimize the error between the predicted output value, f(x m θ) and the actual output value y m. The equation for the error surface is: e m = 1 2 [f(x θ) y]2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
26 Error Suppose that you have the m th training data pair (x,y) G. The GM s goal is to minimize the error between the predicted output value, f(x m θ) and the actual output value y m. The equation for the error surface is: e m = 1 2 [f(x θ) y]2 We seek to minimize e m by choosing the parameters θ that are Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
27 Error Suppose that you have the m th training data pair (x,y) G. The GM s goal is to minimize the error between the predicted output value, f(x m θ) and the actual output value y m. The equation for the error surface is: e m = 1 2 [f(x θ) y]2 We seek to minimize e m by choosing the parameters θ that are b i,cj i and θj i, i = 1,2,...,R, j = 1,2,...,n. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
28 Error Suppose that you have the m th training data pair (x,y) G. The GM s goal is to minimize the error between the predicted output value, f(x m θ) and the actual output value y m. The equation for the error surface is: e m = 1 2 [f(x θ) y]2 We seek to minimize e m by choosing the parameters θ that are b i,cj i and θj i, i = 1,2,...,R, j = 1,2,...,n. R rules, n input variables. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
29 Error Suppose that you have the m th training data pair (x,y) G. The GM s goal is to minimize the error between the predicted output value, f(x m θ) and the actual output value y m. The equation for the error surface is: e m = 1 2 [f(x θ) y]2 We seek to minimize e m by choosing the parameters θ that are b i,cj i and θj i, i = 1,2,...,R, j = 1,2,...,n. R rules, n input variables. θ(k) will be used to denote these parameter s values at time k. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
30 Section Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law 5 Input Membership Function Spreads Update Law 6 Example Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
31 b i Update Law How to adjunt the b i to minimize e m. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
32 b i Update Law How to adjunt the b i to minimize e m. We will use b i (k +1) = b i (k) λ 1 e m b i k Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
33 b i Update Law How to adjunt the b i to minimize e m. We will use where i = 1,2,...,R b i (k +1) = b i (k) λ 1 e m b i k Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
34 b i Update Law How to adjunt the b i to minimize e m. We will use where i = 1,2,...,R b i (k +1) = b i (k) λ 1 e m b i This is the gradiante descent approach. k Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
35 Gradient Descent The update method would move b i along the negative gradient of the error surface. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
36 Gradient Descent The update method would move b i along the negative gradient of the error surface. The parameter λ 1 > 0 characterizes the step size. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
37 Gradient Descent The update method would move b i along the negative gradient of the error surface. The parameter λ 1 > 0 characterizes the step size. If λ 1 is chosen too small, then b i is adjusted very slowly. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
38 Gradient Descent The update method would move b i along the negative gradient of the error surface. The parameter λ 1 > 0 characterizes the step size. If λ 1 is chosen too small, then b i is adjusted very slowly. If λ 1 is chosen too big, then it may step over the minimum value of e m. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
39 Gradient Descent The update method would move b i along the negative gradient of the error surface. The parameter λ 1 > 0 characterizes the step size. If λ 1 is chosen too small, then b i is adjusted very slowly. If λ 1 is chosen too big, then it may step over the minimum value of e m. Some algorithms try to adaptively choose the step size. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
40 Gradient Descent The update method would move b i along the negative gradient of the error surface. The parameter λ 1 > 0 characterizes the step size. If λ 1 is chosen too small, then b i is adjusted very slowly. If λ 1 is chosen too big, then it may step over the minimum value of e m. Some algorithms try to adaptively choose the step size. If the error is big increase λ 1, but if they are decreasing take small steps. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
41 b i Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
42 b i Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Regra da Cadeia: e m b i = (f(x m θ) y m ) f(xm θ) b i Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
43 b i Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Regra da Cadeia: e m = (f(x m θ) y m ) f(xm θ) b i b i Since f(x θ) = R i=1 b n i j=1 exp 1 2 ( x j c i j ) 2 σ j i ( ) R n i=1 j=1 exp 1 x j c j i 2 2 σ j i Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
44 b i Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Regra da Cadeia: e m = (f(x m θ) y m ) f(xm θ) b i b i Since f(x θ) = R i=1 b n i j=1 exp 1 2 then e m = (f(x m θ) y m ) b i ( x j c i j ) 2 σ j i ( ) R n i=1 j=1 exp 1 x j c j i 2 2 σ j i n j=1 exp 1 2 ( x j c i j ) 2 σ j i ( ) R n i=1 j=1 exp 1 x j c j i 2 2 σ j i Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
45 b i Update Formula II Let µ i (x m,k) = ( ) n j=1 exp 1 x j c j i 2 2 σ j i ( ) R n i=1 j=1 exp 1 x j c j i 2 2 σ j i Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
46 b i Update Formula II Let µ i (x m,k) = ( ) n j=1 exp 1 x j c j i 2 2 σ j i ( ) R n i=1 j=1 exp 1 x j c j i 2 2 σ j i Let ǫ m (k) = f(x m θ(k)) y m Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
47 b i Update Formula II Let µ i (x m,k) = ( ) n j=1 exp 1 x j c j i 2 2 σ j i ( ) R n i=1 j=1 exp 1 x j c j i 2 2 σ j i Let ǫ m (k) = f(x m θ(k)) y m Then µ i (x m,k) b i (k +1) = b i (k) λ 1 ǫ m (k) R i=1 µ i(x m,k) Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
48 Section Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law 5 Input Membership Function Spreads Update Law 6 Example Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
49 c i j Update Law We will use c i j (k +1) = ci j (k) λ 2 e m c i j k Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
50 c i j Update Law We will use c i j (k +1) = ci j (k) λ 2 e m c i j k where λ 2 > 0, i = 1,2,...,R and j = 1,2,...,n Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
51 c i j Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
52 c i j Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Regra da Cadeia: e m c i j = ǫ m (k) f(xm θ(k)) µ i (x m,k) µ i (x m,k) cj i Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
53 c i j Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Regra da Cadeia: e m c i j Now f(xm θ(k)) µ i (x m,k) = ǫ m (k) f(xm θ(k)) µ i (x m,k) µ i (x m,k) cj i = ( R i=1 µ i(x m,k))b i (k) ( R i=1 b i(k)µ i (x m,k))(1) ( R i=1 µ i(x m,k)) 2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
54 c i j Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Regra da Cadeia: e m c i j Now f(xm θ(k)) µ i (x m,k) So that f(xm θ(k)) µ i (x m,k) = ǫ m (k) f(xm θ(k)) µ i (x m,k) µ i (x m,k) cj i = ( R i=1 µ i(x m,k))b i (k) ( R i=1 b i(k)µ i (x m,k))(1) ( R i=1 µ i(x m,k)) 2 = b i(k) f(x m θ(k)) R i=1 µ i(x m,k) Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
55 c i j Update Formula II Also we have µ i(x m,k) c i j ( ) = µ i (x m xj,k) m c j i (k) (σj i(k))2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
56 c i j Update Formula II Also we have µ i(x m,k) c i j ( ) = µ i (x m xj,k) m c j i (k) (σj i(k))2 The update formula for c i j is Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
57 c i j Update Formula II Also we have µ i(x m,k) c i j ( ) = µ i (x m xj,k) m c j i (k) (σj i(k))2 The update formula for c i j is Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
58 c i j Update Formula II Also we have µ i(x m ( ),k) cj i = µ i (x m xj,k) m c j i (k) (σj i(k))2 The update formula for cj i is ( ) ( cj(k+1) i = cj(k) λ i b i (k) f(x m θ(k)) x m 2 ǫ m (k) R i=1 µ µ i (x m j cj i,k) (k) ) i(x m,k) (σj i(k))2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
59 Section Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law 5 Input Membership Function Spreads Update Law 6 Example Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
60 σ i j Update Law We will use σ i j (k +1) = σi j (k) λ 3 e m σ i j k Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
61 σ i j Update Law We will use σ i j (k +1) = σi j (k) λ 3 e m σ i j k where λ 3 > 0, i = 1,2,...,R and j = 1,2,...,n Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
62 σ i j Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
63 σ i j Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Regra da Cadeia: e m σ i j = ǫ m (k) f(xm θ(k)) µ i (x m,k) µ i (x m,k) σj i Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
64 σ i j Update Formula I Erro: e m = 1 2 [f(x θ) y]2 Regra da Cadeia: e m σ i j We already calculated f(xm θ(k)) µ i (x m,k) = ǫ m (k) f(xm θ(k)) µ i (x m,k) µ i (x m,k) σj i = b i(k) f(x m θ(k)) R i=1 µ i(x m,k) Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
65 σ i j Update Formula II Also we have µ i(x m,k) σ i j ( ) = µ i (x m (x m,k) j cj i(k)2 ) (σj i(k))3 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
66 σ i j Update Formula II Also we have µ i(x m,k) σ i j ( ) = µ i (x m (x m,k) j cj i(k)2 ) (σj i(k))3 The update formula for σ i j is Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
67 σ i j Update Formula II Also we have µ i(x m,k) σ i j ( ) = µ i (x m (x m,k) j cj i(k)2 ) (σj i(k))3 The update formula for σ i j is Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
68 σ i j Update Formula II Also we have µ i(x m ( ),k) σj i = µ i (x m (x m,k) j cj i(k)2 ) (σj i(k))3 The update formula for σj i is ) 2 σj(k i +1) = σj(k) λ i 3 ǫ m (k) b i(k) f(x m θ(k)) (x R i=1 µ µ i (x m j m cj i(k),k) i(x m,k) (σj i(k))3 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
69 Section Summary 1 Introduction 2 Training Standard Fuzzy System 3 Output Membership Function Centers Update Law 4 Input Membership Function Centers Update Law 5 Input Membership Function Spreads Update Law 6 Example Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
70 Training Data Set We will use the training data set of the table to illustrate the algorithm. x 1 x 2 y x x x Table: Z = [([x 1,x 2 ],y)] Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
71 Choosing the step size The algorithm requires that a step size λ be specified for each of the three parameters. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
72 Choosing the step size The algorithm requires that a step size λ be specified for each of the three parameters. Selecting a large λ will converge faster but may risk overstepping the minimum. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
73 Choosing the step size The algorithm requires that a step size λ be specified for each of the three parameters. Selecting a large λ will converge faster but may risk overstepping the minimum. Selecting a small step means converging very slowly. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
74 Choosing the step size The algorithm requires that a step size λ be specified for each of the three parameters. Selecting a large λ will converge faster but may risk overstepping the minimum. Selecting a small step means converging very slowly. In this example the same value will be chosen, so λ 1 = λ 2 = λ 3 = 1. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
75 Choosing initial values Initial values for the rules must be designated. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
76 Choosing initial values Initial values for the rules must be designated. For the first rule, we choose x 1 1,x1 2,y1 as the input and output membership centers. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
77 Choosing initial values Initial values for the rules must be designated. For the first rule, we choose x 1 1,x1 2,y1 as the input and output membership centers. For the second rule, we choose x 2 1,x2 2,y2 as the input and output membership centers. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
78 Choosing initial values Initial values for the rules must be designated. For the first rule, we choose x 1 1,x1 2,y1 as the input and output membership centers. For the second rule, we choose x 2 1,x2 2,y2 as the input and output membership centers. Select spread equals to 1. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
79 Choosing initial values Initial values for the rules must be designated. For the first rule, we choose x 1 1,x1 2,y1 as the input and output membership centers. For the second rule, we choose x 2 1,x2 2,y2 as the input and output membership centers. Select spread equals to 1. These values correspond to the zero time step (k = 0). Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
80 Choosing initial values [ c 1 1 (0) c 1 2 (0) [ c 2 1 (0) c 2 2 (0) Rule1 ] = Rule2 ] = [ 0 2 [ 2 4 ] [ σ 1 1 (0) σ 1 2 (0) ] [ σ 2 1 (0) σ 2 2 (0) ] = ] = [ 1 1 [ 1 1 ] ] b 1 (0) = 1 b 2 (0) = 5 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
81 Plotting initial values 1 µ(x 1 ) 0.5 c c x 1 1 µ(x 2 ) 0.5 c 21 c x 2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
82 Calculating predicted outputs Calculate the membership values of the implication of each rule using: ( ) n µ i (x m,k = 0) = exp 1 x m j cj i 2 (k = 0) 2 σj i (k = 0) j=1 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
83 Calculating predicted outputs Calculate the membership values of the implication of each rule using: ( ) n µ i (x m,k = 0) = exp 1 x m j cj i 2 (k = 0) 2 σj i (k = 0) j=1 Calculate the outputs using (defuzzification): f(x m θ(k = 0)) = R i=1 b i(0)µ i (x m,k = 0) µ i (x m,k = 0) Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
84 Membership degrees rule 1 [ µ 1 (x 1,0) = exp 1 ( ) ] [ exp 1 ( ) ] = [ µ 1 (x 2,0) = exp 1 ( ) ] [ exp 1 ( ) ] = [ µ 1 (x 3,0) = exp 1 ( ) ] [ exp 1 ( ) ] = Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
85 Membership degrees rule 2 [ µ 2 (x 1,0) = exp 1 ( ) ] [ exp 1 ( ) ] = [ µ 2 (x 2,0) = exp 1 ( ) ] [ exp 1 ( ) ] = [ µ 2 (x 3,0) = exp 1 ( ) ] [ exp 1 ( ) ] = Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
86 Defuzzification f(x 1 θ(0)) = b 1(0) µ 1 (x 1,0)+b 2 (0) µ 2 (x 1,0) µ 1 (x 1,0)+µ 2 (x 1,0) f(x 1 θ(0)) = f(x 1 θ(0)) = f(x 2 θ(0)) = b 1(0) µ 1 (x 2,0)+b 2 (0) µ 2 (x 2,0) µ 1 (x 2,0)+µ 2 (x 2,0) f(x θ(0)) = f(x 2 θ(0)) = Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
87 Defuzzification f(x 3 θ(0)) = b 1(0) µ 1 (x 3,0)+b 2 (0) µ 2 (x 3,0) µ 1 (x 3,0)+µ 2 (x 3,0) f(x 3 θ(0)) = f(x 3 θ(0)) = Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
88 Calculating erros e m = 1 2 [f(xm θ(k = 0)) y m ] 2 e 1 = 1 2 [ ]2 = e 2 = 1 2 [ ]2 = e 3 = 1 2 [ ]2 = Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
89 Calculating erros e m = 1 2 [f(xm θ(k = 0)) y m ] 2 e 1 = 1 2 [ ]2 = e 2 = 1 2 [ ]2 = e 3 = 1 2 [ ]2 = The first two data points are mapped better than the third. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
90 Calculating erros e m = 1 2 [f(xm θ(k = 0)) y m ] 2 e 1 = 1 2 [ ]2 = e 2 = 1 2 [ ]2 = e 3 = 1 2 [ ]2 = The first two data points are mapped better than the third. The result can be improved by cycling through the model. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
91 Calculating erros e m = 1 2 [f(xm θ(k = 0)) y m ] 2 e 1 = 1 2 [ ]2 = e 2 = 1 2 [ ]2 = e 3 = 1 2 [ ]2 = The first two data points are mapped better than the third. The result can be improved by cycling through the model. The GM will update the rule-base parameters b i,c i j and σ i j using the first time step. Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
92 Updating... ǫ m (k = 0) = f(x m θ(k = 0)) y m ǫ 1 (0) = = Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
93 Updating b i b i (k) = b i (k 1) λ 1 (ǫ k (k 1)) µ i (x k,k 1) R i=1 µ i(x k,k 1) µ 1 (x 1,0) b 1 (1) = b 1 (0) λ 1 (ǫ 1 (0)) µ 1 (x 1,0)+µ 2 (x 1,0) ( ) 1 = 1 1 ( ) = µ 2 (x 1,0) b 2 (1) = b 2 (0) λ 1 (ǫ 1 (0)) µ 1 (x 1,0)+µ 2 (x 1,0) ( ) = 5 1 ( ) = Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
94 Updating c 1 j [ ] cj i (k) = ci j (k 1) λ b i (k 1) f(x k θ(k 1)) 2(ǫ k (k 1)) R i=1 µ i(x k,k 1) ( ) x k µ i (x k j cj i (k 1),k 1) (σj i (k 1))2 ( c1 1 (1) = c1 1 (0) 1ǫ b1 (0) f(x 1 ) ( θ(0)) x 1(0) µ 1 (x 1,0)+µ 2 (x 1 µ 1 (x 1 1,0) 1 c1 1(0) ),0) (σ1 1(0))2 c1 1 (1) = 0 ( c2 1 (1) = c2 1 (0) 1ǫ b1 (0) f(x 1 ) ( θ(0)) x 1(0) µ 1 (x 1,0)+µ 2 (x 1 µ 2 (x 1 1,0) 2 c2 1(0) ),0) (σ2 1(0))2 c 1 2 (1) = 2 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
95 Updating c 2 j ( c1 2 (1) = c2 1 (0) 1ǫ b2 (0) f(x 1 ) ( θ(0)) x 1(0) µ 1 (x 1,0)+µ 2 (x 1 µ 2 (x 1 1,0) 1 c1 2(0) ),0) (σ1 2(0))2 c1 1 (1) = ( c2 2 (1) = c2 2 (0) 1ǫ b2 (0) f(x 1 ) ( θ(0)) x 1(0) µ 1 (x 1,0)+µ 2 (x 1 µ 2 (x 1 1,0) 2 c2 2(0) ),0) (σ2 2(0))2 c2 2 (1) = Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
96 Updating σ i j [ ] σj i (k) = σi j (k 1) λ b i (k 1) f(x k θ(k 1)) 3(ǫ k (k 1)) R i=1 µ i(x k,k 1) ( ) (x k j cj i (k 1))2 σ 1 1 (1) = 1 σ 1 2 (1) = 1 µ i (x k,k 1) σ1 2 (1) = σ2(1) 2 = (σj i (k 1))3 Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
97 The End Adriano Cruz (PPGI-UFRJ) Gradient Method 09/ / 41
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