Artificial neural network
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第八章 類神經網路 (Artificial Neural Network) PowerPoint PPT Presentation


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第八章 類神經網路 (Artificial Neural Network). 壹、緒論. 主宰人類思考及行為的大腦,是人類經過數百萬年進化的結晶,最初人類並不相信它是思維和情緒的中心,但在十七世紀以後,經過一些醫生及解剖學家的研究及努力,於是對於腦的結構、腦的基本元素 ‥ 神經元的功能及神經元,組成網路時的連接機能,有了較深入的瞭解,也因此產生了 《 突觸理論 》 ( synaptoldgy ) 的學說,為現代的類神經科學奠訂了基礎。

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第八章 類神經網路 (Artificial Neural Network)

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Artificial neural network

(Artificial Neural Network)


Artificial neural network

  • synaptoldgy


Artificial neural network

  • 501943McCullochPittsMP1949HebbHebb1957Rosenblatt (Perceptron);1962Widrow


Artificial neural network

  • 1969MinskyPerceptronX0RMinskyVonNeumann


Artificial neural network

  • :GrossbergKogonenFukushimaFukushimaBSBAndersonBP(BackPropa-gation)Webos


Artificial neural network

  • 701982HopfieldRumellhartMcClelland(PDP)KoskoHecht-NielsenHolland...


Artificial neural network

  • :1986SnowBird1987SanDiego...:1988""19903IEEE...


Artificial neural network

  • 10111:

  • 1:

  • 2:

  • 3:


Artificial neural network

1


Artificial neural network

  • 4:/

  • 5:20100mV

  • 6:()


Artificial neural network

  • ()()2X1X2Xniijjj


Artificial neural network

X1

X2

Xi i

Xn

2


Artificial neural network

  • i3


Artificial neural network

  • 3

Yi


Artificial neural network

  • net = 1

  • Yi = f(net)

    f(activation function)24


Artificial neural network

Yi

1

4


Artificial neural network

  • g()(net function):

  • 1.net = = XT = 2

  • ()(Linear Model)

  • y = f(net)= net 3


Artificial neural network

5


Artificial neural network

  • ()M-P (M-P model ; Mc Culloch-Pitts,1943)Hard Limiter

y =

1

6 M-P model


Artificial neural network

  • ()

  • y = sgn(net) 4

  • :

  • sgn(x) =


Artificial neural network

Y

1

Net

-1

7


Artificial neural network

  • ()Sigmoid Function

  • M-P()8


Artificial neural network

Y

Y

1

1

Net

Net

-1

(a)

(b)

8 sigmoidfunction


Artificial neural network

  • a

  • y = f(net) =

  • b

  • y = f(net) = -1

  • =2.71828


Artificial neural network

  • ()(Ramp Function)

  • y = f(net) =

9


Artificial neural network

597101010

920821


Artificial neural network

()(Feedforward Network)

10(Percetron)


Artificial neural network

1ii

1ii

10


Artificial neural network

()(Feedback Network)

Hopfield11

11


Artificial neural network

(SupervisedLearning)

12 ()

d 0()


Artificial neural network

12


Artificial neural network

()ood


Artificial neural network

13

/(Training Pattern) oidi


Artificial neural network

13r oix r xdi

r = r(oidI) = r( xdI ) (11)


Artificial neural network

13


Artificial neural network

r

12

c c c

r()


Artificial neural network

1(Hibbian)

r = 0i 13

2

(Perceptron Learning Rule)

r = (di-oi)14

3Delta learning rule

r = 15

f

4Widrow-Hoff.

r = di- 16

5(correlation rule)

r di 17


Artificial neural network

1957F.Rosenblatt2 (Single-Layer Percetron) 10 M-P

y = 18


Artificial neural network

14x1x2:

y = sgn( ) = 19

F.Rosenblatt s s N


Artificial neural network

14


Artificial neural network

:AND15

XOR16


Artificial neural network

15 AND


Artificial neural network

16 XOR


Artificial neural network

XOR 17


Artificial neural network

  • 112 > 1.5

  • 012 1.5

  • 1122y> 0.5

  • 0122y0.5


Artificial neural network

17 XOR


Artificial neural network

n

ii

i=1

n

ii>

=1

n

ii

i=1


Artificial neural network

11 211.5

n

( ii)

i=1

n

( ii1.5

i=1

f(121.5)

1f (001.5)f(-1.5)0

2f011.5f(-0.5)0

3f (101.5)f(-0.5)0

4f (111.5)f(0.5) 1


Artificial neural network

10201-1.50

10212-0.50

11203-0.50

112140.5 1


Artificial neural network

ii2

i=1

1220.5

10+0-0-0.5-0.50

20+1-0-0.50.5 1

31+0-0-0.50.5 1

41+1-2-0.5-0.50

XOR


Artificial neural network

(XOR GATE)

:010 (00111100)XOR(11000011)(11111111)X1X2Y:


Delta learning rule

Delta Learning Rule

r =

Neti = =

Oi = f(neti)di


Artificial neural network

()

E =

() Oi EOi E E0


Artificial neural network

()

r= -

1

2


Artificial neural network

  • E() =0

  • 1. -

  • 2. -


Artificial neural network

()E= =

r=- =- 2

=+


Artificial neural network

()XORdW,32


Artificial neural network

1

2


Artificial neural network

3


Artificial neural network

4

514


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