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Нейросетевые технологии в обработке и защите данных PowerPoint PPT Presentation


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Нейросетевые технологии в обработке и защите данных. Защита информации иммунными нейронными сетями Лекция 1 2 . Особенности механизма вывода в системах н ечеткого вывода. Классический подход к реализации нейронечетких классификаторов.

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Нейросетевые технологии в обработке и защите данных

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5662027

12.


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, .

, .


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- , if then, , :

x A, B,

A, B , x .

, .


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:

A B.

, , -.

N xi : x1 A1x2 A2 xN AN, B.


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A B (x, y)

:


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, , .

, .


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, . , , , , .

. .


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, , , , , .

, , . . ( Max).


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  • ;

  • ;

  • ;

  • ;

  • ;


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.

, .


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:

Rule_1:IF Condition_1 THEN Conclusion_1 (F1 )

Rule_2:IF Condition_2 THEN Conclusion_2 (F2)

Rule_n:IF Condition_n THEN Conclusion_n (Fn)

Fi (i{1,2,,n}) , [0, 1]. , 1.

, P, V, W, , .


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Fazzification

(Fazzification)

.

, , , . , , .


Fazzification1

(Fazzification)

. iis , , ai (x), .


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:

  • ;

  • ;

  • 1 - . 1 is 1, 1is 2,1is 3. 55/.


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1

0.8

0.6

0.4

0.2

0

1)

b1

0 10 20 30 40 50 60 70 80 90 100

a1

1

0.8

0.6

0.4

0.2

0

2)

b1

0 10 20 30 40 50 60 70 80 90 100

a1

1

0.8

0.6

0.4

0.2

0

3)

b1

0 10 20 30 40 50 60 70 80 90 100

a1


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0, a1=55/.

0.67.

0, a1=55/. 3.


Aggregation

(Aggregation)

.

, , ( ).


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  • ;

  • 1 ,2 .

    a1= 55 /. , a2 = 70.


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0.67 0.8.

= 0.8, 0.67 0.8.


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1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

b2

b1

b1"

0 40 50 60 0 30 40 50 60 70 80 90 100

a1 a2 TC

1)

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

b2

b1"

b1

0 40 50 60 0 30 40 50 60 70 80 90 100

a1 a2 TC

2)


Activation

(Activation)

. , . Fi ( 1 ).


Activation1

(Activation)

, C = {c1, c2, , cq} q , .

.

min : (y) = min{ci, (y)}

prod : (y) = ci (y)

average :(y) = 0.5 (ci + (y))


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:

IF THEN

1 , 2 . a1= 55/.


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= 0.67, c1 .

, min , , prod , .


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1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

b1"

b1

0 40 50 60 0 30 40 50 60 70 80 90 100

a1

TC

1)

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

b1"

b1

0 40 50 60 0 30 40 50 60 70 80 90 100

a1

TC

2)


Accumulation

(Accumulation)

W ={w1, w2,, wp}. p . , .


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c1, c2,c3, , .


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1

0.8

0.6

0.4

0.2

0

0 10 20 30 40 50 60 70 80 90 100

1

0.8

0.6

0.4

0.2

0

0 10 20 30 40 50 60 70 80 90 100

1

0.8

0.6

0.4

0.2

0

0 10 20 30 40 50 60 70 80 90 100

1

0.8

0.6

0.4

0.2

0

0 10 20 30 40 50 60 70 80 90 100


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max c1, c2,c3 , .


Deffuzzification

(Deffuzzification)

(crisp) W = {w1, w2,, wp}.

, ( , .).


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:

n , .


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y = u, u

, , , . . .


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1

0.8

0.6

0.4

0.2

0

0 10 20 30 40 50 60 70 80 90 100

.


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-

-

()

()

()

-( ):

x A, yB

A B

:

35


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( Min) x1,x2 , x, A B .

, 1 2, ( Max).


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--

-

min

A1B1C1 C1C1C1

1

x y

z1 = a1 x0 + b1 y0

C2 C2C2C2

2

z2 = a2 x0 + b2 y0

x0 x y0 y

max

max

zc-


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1. ;

2. ;

3. , x;

4. A B;

5. () ;

6. . .


Larsen

(Larsen)

1. ;

2. ;

3. . , , ;

4. prod

5. (max) ;

6. .


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(TSK)

  • . :

    If 1is and 2 is then w = c1 a1 + c2 a2

    c1, c2 , w ;

    2. ;

    3. (min - );


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(TSK)

4. min , , a1,a2 ;

5. ;

6. .

.


Tsukamoto

(Tsukamoto)

  • ;

  • ;

  • . ;

  • min . wj ci = (wj) (i {1,2,,q})

    q ;

    5. ;

    6. . .


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, prod .

(TSK) , , .


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ANFIS (Adaptive-Network-Based Fuzzy Inference System) Fuzzy Logic Toolbox ( ) MATLAB . ANFIS - . Fuzzy Logic Toolbox Mamdani , , Sugeno .


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