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第三章 前馈型神经网络模型. 3.1 感知器( Perception ) 3.2 多层前馈型神经网络 3.3 误差逆传播算法( BP 算法) 3.4 误差逆传播算法 (BP 算法 ) 的若干改进 3.5 使用遗传算法 (GA) 训练前馈型神经网络方法 3.6 前馈型神经网络结构设计方法. 3.7 基于算法的前馈型神经网络在识别问题中的应用 3.8 自适应线性元件 3.9 径向基函数神经网络. 3.1 感知器( Perception ). 3.1.1 单层感知器 3.1.2 感知器的收敛定理 - PowerPoint PPT Presentation
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3.1 Perception 3.2 3.3 BP3.4 (BP)3.5 (GA)3.6
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3.7 3.8 3.9
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3.1 Perception 3.1.1 3.1.2 3.1.3 3.1.4
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3.1.1 X=X1,X2,,XmY=(Y1,Y2,,Yn) XRn, WRnY{1,-1} X= (X,-1),W= (W,)
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w21wmjw22wmnw12w11xmx1x2w1nw2mwmjwijw2jw1jyjxix1x2xm 3.1 3.2 wm1
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Wn+1=, Xn+1=-1, Wi(0)Wi(t)ti1in,Wn+1(t)t X=(X1,X2,,Xn,T),T XAT=1XBT=-1
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Y=W1X1+W2X2- 3.1
000011101110
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3.1.2 XYWf fRn {1,-1}, XRn, f X1W1+X2W2+X3W3= X1W1+X2W2+(X1X2)W3=
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3.2
0000010110011110
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3.4
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3.1 f (1)Xk=1() (2)Yk0(f)
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kW*kW*Xk> >0 t=1W(t)0 k{1,N}X(t)=Xk W(t)X(t)0 W(t+1)=W(t)+X(t),t=t+1,
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C(t)W(t)W*
W*W(t+1)=W*[W(t)+X(t)]=W*W(t)+W*X(t)W*W(t)+ W*W(t)tW(t+1)2=W(t)2+2W(t)X(t)+X(t)2
3.1.3 nn1n2 (j=1,2,,n1) (k=1,2,,n2)
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(A) 3.5
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3.2 3.5(B)
nn1nnn=2n1=3j (j=1,2,3)
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(B) 3.5
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Wijj3.6(A)AB 3.6(B)
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Y2={(X1,X2)[(W11X1+W21 X2 -1)>0(W12 X1+ W22 X2- 2)>0(W13 X1+W23 X2-3)>0]} Y3={(X1,X2)[Y12Y22]} 3 6 ={(X1,X2)[((W1jX1+W2j X2 -j )>0)((W1j X1 j=1 j=4 +W2j X2- j )>0)]}
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(A) (B) 3.6
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Y11=1X1+1 X2-1 Y21=(-1) X1+(-1) X2-(-1.5) Y2=1 Y11+1 Y21-2
3.7
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3.8
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3.1.4 3.9
3.9
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u = W1X1+W2X2
(0.5, 0.05)(0.05, 0.5) A (0.95,0.5)(0.5,0.95) B
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W1(t+1)=W1(t)+(T-Y)X1 W2(t+1)=W2(t)+(T-Y)X2 (t+1)=(t)+(T-Y)
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3.3 (a) 3.3 (b)
X1X2YW0.200.500.050.990.100.050.500.99u00.200.950.500.010.400.50 0.95 0.010.300.01 200
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3.2 3.2.1 3.2.2 3.2.3
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3.2.1 XK TKK{1,2,,N}NXKRnTKRm
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3.12
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Sigmoid(-1,1)
( -1 < f(x)
3.4
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3.2.2 E (W) =g ( f ( W, XK, TK ) ),K=1,2,,NEYKTKNm
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(XK , TK) , (K=1,2,,N)W{XkRn}{YkRm} f XkRn YkRm
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mn2XK,TKW(W1,W2,.,W mn2)E(W)(mn2+1)mn2+1E (W) WE(W)E(W)E(W)
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3.2.3 E(W)W
N(XK, TK), K=(1,2,,N), XKYKiYiKijWijj
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1j
2j
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3.3 BP 3.3.1 BP3.3.2 BP
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3.3.1 BP Sigmoid
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Yi1i Yj2j Yk3k Tkk Wijij Wjkjk j j k k 3.14
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t=0Wij(t)[-1,1]Wjk(t)[-1,1],j(t)[-1,1], k(t)[-1,1] () XK,TK,K{1,2,,N}NXKRnTKRm
j{1,2,,n1}
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k{1,2,,m}
() k{1,2,,m}
kWjkkkjWjkk
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jWijjjiWijj EEEt = t+1
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WEYNNYYN3.15 BP
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3.153.16
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Memond(t-1)W(t-1)tW(t)MemondmMemond W (t+1) = W(t)+mW(t-1) tW(t)MemondW(t-1)W(t)Memond W (t+1) = W(t)+m(t)W(t-1) m(t) = m + m(t-1)
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u0Memond m=0.9 memondm=0.6 m=0.02 0.55531 0.6 4743 0.7 63 61
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3.3.2 BP 3.4 (X)KRnf(X)=f(X1,X2,,Xn)K>0NCii(i=1,2,,N)Wij(i=1,2,,N;j=1,2,,n)
(3.3.16) >0(X)fRnRm
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BP 1 2 3
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4 5
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3.4 (BP) 3.4.1 BP 3.4.2 BP 3.4.3 BI(Back Impedance)
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3.4.1 BP 1
0.9 (t+1)(t)
- / (1- )
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2 =1.5 / =0.9 3 (0)(0)/(t+1) (t) = (0)/(t+1)
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4
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()
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3.4.2 BP 1 ij(0) ij(t)= ij(t-1) u
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u1/d1.11.30.7-0.9 2DeltaBarDelta DeltaBarDelta ij(0)
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(t)
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u(5.00.0950.0850.035)d (0.90.850.666)0.7u
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3.4.3 BI(Back Impedance) 1BI
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Wij(t+1)= Wij(t) + aj Yi + b(Wij(t) - Wij(t-1))+ c(Wij(t-1) - Wij(t-2)) abc abc a= 1 / (1+J+M+D) b= (2J+M) / (J+M+D) c= J / (J+M+D)
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JMD
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3.18
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2
X=[0,1]ABCD=KX+PA=0.5, B=0.75, C=3, K=-5, P=12BI3.6 99.206%
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3.6
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3.5 (GA) GA(chromosome)(gene) (Population)(N)GA GA(fitness function)
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(Coding) (Selection) (Crossover)
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(Mutation) (1)NS (2)S
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GA
3.20
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35164j (3.5.1) k (3.5.2) Sigmoid (3.5.3)
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GA 1. 35*16+16*4+16+4-1.0+1.0 2.
i=1,2,,Np=1,,4K=1,2,3,4Tk
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3. N=10K1 4. (1)10-1.0+1.0 (2)E(i)f (i)
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3.22
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3.23
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3.24
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3.6 3.6.1 3.6.2
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3.6.1 mmxxjj
j10mlog2 m
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3.6.2 3.4 SI/O
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l1987HechtNielsenANN2N+1N
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21987RPLinnmannM2M H=log2T HT 31988Kuarycki31
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BP208Linnmann M2=82=16Kuarycki3M2=48 4ANN 1990NelsonIllingworth4NANN0.02N444=16646420
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5 LippmannM1(N+1) KuaryckiM13 A .J.Maren(M1N)1/2M1
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6 n K< C in1Kn1n i=1 i >n1 Cin1=0 mn110 n1=log2n n
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BP
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3.8
xk,wkRn,k k=w0k , x0=1
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3.44 Adaline
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1 XkRn XkRn+llXkxik i=1,2,,nn=2F1F2 F3 3.45(a) F1=x1k2 F2=x1kx2k F3=x2k2 yk Yk= wik xik+w11 F1+w12 F2+w22 F3 i=0 =w0k+x1kw1k+x2kw2k+w11x21k+w22x22k+ w12x1kx2k yk=0
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3.45aAdaline(b)XOR(c) AAND
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n 2n+C2n=2n+n(n-1) / 12 = (n2+3n) /2 2Madaline Adaline
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3.46 (a)Madaline (b)Madaline
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Adaline 1Adaline 2XkTk(3.8.1)Tk 30 4
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50 6 7 8MRIIAdalineLMS
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w(t0+1) w(t0)Xk(t0)(t0)t0 Xk(t0) +1-1X(t0)2 X(t0 )2=x12(t0 )+ x22(t0 )++ xn+12(t0 )=n+1 3.8.5(t0) (t0)=Tk(t0)-XT(t0)W(t0)
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(t0)= [T(t0 )-XT(t0 )W(t0 )]=-XT(t0 )W(t0 ) W(t0 )=W(t0+1)-W(t0 )=( / Xk(t0 )2 )(t0 )X(t0 ) (t0)=- XTk(t0 ) [ / Xk(t0 )2] (t0) Xk(t0 )=- (t0) 1.0>>0.1
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3.9 3.9.1 3.9.2
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3.9.1 3.49
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3.9.2 b CK=| CK1 ,CK2 ,, CKn | K=1,2,N X
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Gauss f (x)=exp(-x2 / b)=exp(- 2K / b) Gaussb yK=f (K) j
Wj =[w1j ,w2j ,, wNj ] , Z=[z1 , z2 ,, zN ]
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3.50 Gauss
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RBFRBFbb RBF bwBP
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3.7 3.7.1
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3.7.1 2004l
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3.26
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1 3.27(a)abcd (3.7.1)
l010
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3.27 jl5
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2 ni5nij (j=1,,5)TiTin1Ti (1)niVij(j=1,,5)
j=15 (3.7.2) Ti kp,kp=1,,5(3.7.3)
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(2) n1Vij'0.2Vij0.2Ti (3.7.4) Ti 3
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3.28 2.29 (a)
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3.30
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3.31
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AC-l+lA'=A+0.5CAA'10003.32
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3.32
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1
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3.13
00.196909O.2558330.257407O.1447570.18260810.2006180.2525650.2558010.1512540.18555720.4887890.3021450.6858840.7380451.01677230.2309640.2930880.2814020.1736100.204479220.2220850.2688120.2636200.1648590.186757230.3012080.3679700.3492080.2431080.271468240.6160160.6106360.3677400.5664090.321578
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3.14
1000001000001000001000001
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3.33
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2 f(x)=1/(1+exp(-x/U0)) (3.7.5) (1)WjiVkjjk (2)1 (3) Uj= Wji Ii+ j (3.7.6) Hi=f(Uj) (3.7.7)
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3.34
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(4) Sk= Vkj Hj + k , Ok=f(Sk) (3.7.8) (5)TkOkkVkjk k : k=(Ok Tk) Ok (1Ok) (6)kVkjHjjWjij j : j=k Vk Hj (1- Hj) (3.7.9)
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(7)kVkjjHjVkjkj Vkj= Wji + k Hi , k = k + k (3.7.10) (8)jWjijIiWjijj Wji= Wji+j Ii , j = j + j (3.7.11)
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(9)E E= Ok - Tk (3.7.12) E(2)(3)
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3.35
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3
3.36
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3.21
0.9520790.0221980.0213390.0204230.0l0000 0.0283280.9816660.0100000.0100000.0100000.0106780.0100000.9900000.0100000.0100000.0100000.0100000.0100000.9874710.0387660.0100000.0100000.0147230.0100000.990000
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1.(10000000) 2.(01000000) 3.(0099800) 4.(0009980) 5.(00001000)
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3.37
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