[资料共享]附葛德彪书后程序，新手交流！

运行成功的意思是？画的图也是对的吗？貌似程序里还有点问题啊

老是出现错误怎么回事错误    254    Compilation Aborted (code 1)    

谢谢了，偶也是新手

看看，学习下。。

感谢您的资料

我下载不了  可以发一个给我嘛？563179536@qq.com  谢谢了

for index = 0:20                           $A2n{  
P0 = index/20;                             l30Y8t~d  
P1 = 1 - index/20;                              %计算发送1的先验概率P1 !R'g59g  
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 ${I*nh>=  
%%%计算贝叶斯风险表达式中的相关常量%%% H4:&%"j7  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); c6&Q^p|CF  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); thc <xxRP  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); =Mj0:rW  
old_RB = 0; r^0F"9eOL  
whileindex = 0; yVX8e I  
%%%求解贝叶斯风险值%%% WBr59@V  
while  (1) D`[Khsf  
whileindex = whileindex + 1;     ]y#3@  
P = zeros(3,2,2); aUc|V{Jp  
for i = 1:3 ~g6 3qs  
    %%% P(i,j,k)表示，对于第i个传感器，发送k-1，而判决为j-1的概率。%%% (UL4+ta  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf 3BG>Y(v  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   DF_X  
                                           %计算第i个传感器的漏报概率Pm t$J.+}}I  
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf 6*45Vf  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd OhVs#^  
end =-"c*^$]  
]
fBUT6  
% 计算融合中心的P(u|H1) W/fuKGZi_  
Pu_h1 = zeros(2,2,2); Qkcjr]#^$  
for i = 1:2 _%/}>L>-`8  
    for j = 1:2 j$Ab>}g]  
        for k = 1:2 wSEWwU[  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           cl@
g  
        end j8Cho5C  
    end ^7^N}x@  
end k*?I>%^6#T  
-0Cnp/Yj@  
% 计算融合中心的P(u|H0) K/L;8a  
Pu_h0 = zeros(2,2,2); :e<7d8E5n{  
for i = 1:2 Ts.wh>`  
    for j = 1:2 {pL+2%`~  
        for k = 1:2 h6~$/`&]b  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); 1oiRWRe  
        end 'Gl&Pa1g?  
    end l+*&:Q/  
end 58 bCUh#uw  
&V7M}@  
% 计算融合中心的P(u0=1|u)% I_1e?\  
Pu0_1_u = zeros(2,2,2); A\fb<  
for i = 1:2 i,IB!x  
    for j = 1:2 FAsFjRS  
        for k = 1:2 b2,!g }I  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); - e"jw#B  
            if(judge < 0) Djq!P  
                Pu0_1_u(i,j,k) = 1; 3]RyTQ
 
            else q_kdCO{:df  
                Pu0_1_u(i,j,k) = 0; zZ=pP5y8  
            end X]%itA  
        end Lfog {Vzs  
    end 8NBT|N~N  
end F@ Swe  
2ZcKK8X;7  
% 计算融合中心的贝叶斯风险 bi[IqU!9  
RB = C; y5r4+2B  
for i = 1:2 vi.w8>CE  
    for j = 1:2 >J9oH=S6  
        for k = 1:2 I,yC D7l_  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); iOAbaPN  
        end /D!;u]  
    end nKa$1RMO  
end +{<#(}  
+N`ua  
% 计算A_ui #@HF<'H}mu  
for i = 1:2 z2_6??tS/c  
    for j = 1:2 EU7|,>a  
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); km~Ll  
    end -m *Sq  
end ':{>a28=  
for i = 1:2 >P6BW  
    for j = 1:2 Ae]sGU|?'  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); VTy9_~q  
    end zk-.u}RBFG  
end kF(n!2"W  
for i = 1:2 7lV.[&aKW  
    for j = 1:2 1#IlWEg  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); AIMSX]m  
    end -Eu6U`"(  
end Ar*^;/  
>zAUW[]C:I  
% 计算P(ui|h0) tW WW
x~k  
for i = 1:2 mKrh[nA  
    for j = 1:2 wLc4Dm*V  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); CI
353-`
 
    end *g?Po+ef%  
end 0}!\$"|D  
for i = 1:2 o1M$.*  
    for j = 1:2 wLK07e(  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); "&/-N[is  
    end ;AO#xv+#  
end P3$Q&^?  
for i = 1:2 ~ab:/!Z  
    for j = 1:2 [8tL"G6s  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); hxQqa 0B  
    end Z}$TKO*u  
end !;?+>R)h  
% 计算P(ui|h1) [sB 9gY(  
for i = 1:2 C8 \5A8c  
    for j = 1:2 VD_$$Gn*q  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); okbQ<{9  
    end |$?bc3  
end {~Rk2:gx  
for i = 1:2 O T.*pk+<)  
    for j = 1:2 _S>JKz  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); 93Co}@Y;Y+  
    end /c uLc^(X  
end +
$g}4  
for i = 1:2 [Xb@Wh:yG  
    for j = 1:2 qkiI/nH3  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); ZK>WW  
    end BD(Z5+EU1  
end >=[(^l  
%%%计算新门限值T %%% uEX!xx?Q#  
numerator = zeros(1,3); ty8v 6J#  
denominator = zeros(1,3); |PC*=ykT3  
for i = 1:2 j~!X;PV3  
    for j = 1:2 %Dwk  
        %% 计算第1个传感器新门限值分子与分母的求和部分 `@nl  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); YW7b)uYf  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); tQS5hwm*  
    end @Y1s$,=xB  
end /`mks1:pK  
for i = 1:2 z11O F  
    for j = 1:2 I:mr}mv=i  
        %% 计算第2个传感器新门限值分子与分母的求和部分 ?Y@N`S  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); \[y`'OD~  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); 6I%5Q4Ll  
    end gLOEh6  
end l<A|d{"]  
for i = 1:2 hEp(A8g)bQ  
    for j = 1:2 D8$G`~hD  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); 'FDef#P<  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); rk #sy$  
    end G K
7![p  
end {WYX~Mvvj  
if(denominator(1) == 0) _H5o'>=  
     break; zG(\+4GE!  
end
S:OO0<W  
if(denominator(2) == 0) 9jw\s P@  
     break; H'  
end DAXX;4  
if(denominator(3) == 0) /Njd[=B  
     break; $m/)FnU/  
end 3.*8)NW  
for i = 1:3 Q\ 0cvmU  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); u fw]=h)  
end abyo4i5T  
if(abs(RB-old_RB) < epsilon) zx)z/
1  
    break; !O<)\)|g  
else INJEsz  
    old_RB = RB; !qWH`[:  
end ~{jcH  
if(whileindex == 20) x^y'P<ypw  
    break; "thdPZ  
end c-(UhN3WG  
NfjE`  
end sVOyT*GY  
result(index+1) = RB; (jAg_$6  
end  |'B7v i)  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 ^zQ/mo,Z  
x = 0:0.05:1; Z'.AAOG  
plot(x,result,'r'); :51/29}  
title('贝叶斯风险值-先验概率P0曲线'); njputEGX  
xlabel('先验概率P0'); R}!:'^  
ylabel('贝叶斯风险值'); fTK3,s1=  
for index = 0:20                           -w"VK|SGm  
P0 = index/20;                             2Eu`u!jhx  
P1 = 1 - index/20;                              %计算发送1的先验概率P1 ++`0rY%  
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 e]zBf;9J  
%%%计算贝叶斯风险表达式中的相关常量%%% =`H@%  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); 
Zg{KFM%  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); .oeX"6K  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); oU.R2\Q  
old_RB = 0; w|K'M?N14  
whileindex = 0; oYH^_V  
%%%求解贝叶斯风险值%%% NgP&.39U  
while  (1) pC@{DW;V6R  
whileindex = whileindex + 1;     {#@W)4)cA  
P = zeros(3,2,2); A;]}m8(*  
for i = 1:3 *^VRGfpb
 
    %%% P(i,j,k)表示，对于第i个传感器，发送k-1，而判决为j-1的概率。%%% nH[yJGZYSA  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf +l<5#pazx  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   Na]:_K5Dp  
                                           %计算第i个传感器的漏报概率Pm ^LoUi1j  
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf +[\FD; >  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd v0H@Eg_  
end "W955?4m  
#ML%ij 1  
% 计算融合中心的P(u|H1) D$I5z
.a  
Pu_h1 = zeros(2,2,2); w=T\3(%j  
for i = 1:2 %z,mB$LY  
    for j = 1:2 klT@cO-9  
        for k = 1:2 /}b03  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           GLeK'0Q@  
        end pL/DZ|S3  
    end *V8<:OG|e  
end KUlp"{a`,K  
>I-RGW'A  
% 计算融合中心的P(u|H0) j i7[nY  
Pu_h0 = zeros(2,2,2); 0dE@c./R i  
for i = 1:2 of%Ktm5Qi  
    for j = 1:2 a%/9v"}  
        for k = 1:2 Y[}>CYO  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); T[UN@^DP(  
        end Ch<[l8;K  
    end \o*5  
end Oi8.8M  
"K6&dk jY  
% 计算融合中心的P(u0=1|u)% vqDu(6!2  
Pu0_1_u = zeros(2,2,2); YIQ 4t  
for i = 1:2 keL&b/@  
    for j = 1:2 A$Hfr8w1u  
        for k = 1:2 qNB<T('  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); \`~Ly-  
            if(judge < 0) 1WxK#c-)  
                Pu0_1_u(i,j,k) = 1; 8]/bK5`  
            else v3~?;f,l  
                Pu0_1_u(i,j,k) = 0; SB H(y)  
            end W$hx,VEy`  
        end 1\ o59
Y  
    end Jh,]r?Bd  
end 0zmE>/O+
 
9
6( v  
% 计算融合中心的贝叶斯风险 r!_-"~`7E  
RB = C; tE<H|_{L  
for i = 1:2 qr"3y  
    for j = 1:2 6t[+pL\b  
        for k = 1:2 zPn+V7F  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); [+T.at  
        end lb4Pcdj  
    end Lo{ E:5q  
end ` Mjj@[  
iT3BF"ZqBO  
% 计算A_ui %[Wh [zZy  
for i = 1:2 2_HNhW  
    for j = 1:2 ayR-\mZ  
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); 5F)C  jQ  
    end y" RF;KW>  
end riY~%9iV'  
for i = 1:2 vdivq^%=a  
    for j = 1:2 `u3E
U*~W  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); x<tb  
    end KX,S  
end IA8f*]?  
for i = 1:2 `2("gUCm  
    for j = 1:2 Gp?a(-K5  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); <\rT%f}3^  
    end ?+@n3]`0  
end 2=,lcWr  
_S<3\%(0  
% 计算P(ui|h0) 4gI/!,J(b  
for i = 1:2 kCWV r  
    for j = 1:2 <wN}X#M  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); ]b2pG'  
    end LG{,c.Qj*  
end ey7 f9  
for i = 1:2 N.,X<G.H  
    for j = 1:2 c}lb%^;)E  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); BO~0ON0  
    end ?~BC#B\>o  
end 6Z
m# bFQ  
for i = 1:2 ElcjtYu4  
    for j = 1:2 kbX8$xTM  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); yj 3cyLXw  
    end X`kk]8=  
end )B"jF>9)[  
% 计算P(ui|h1) aH)}/n  
for i = 1:2 KrgFKRgGj  
    for j = 1:2 Q{sH3Y#l  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); deBY5|  
    end ,1~"eGl!  
end (y=C_wvqZ  
for i = 1:2 V\ZGd+?  
    for j = 1:2
{f/~1G
[M  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); k9sh @ENy  
    end >?2M }TV3  
end > kGGR  
for i = 1:2 1g
L2ia  
    for j = 1:2 "jeb%k  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); !#], hok8X  
    end dyz2.ZY~2  
end @Q)OGjaq  
%%%计算新门限值T %%% (9''MlGd%  
numerator = zeros(1,3); + [iQLM?zo  
denominator = zeros(1,3); w1Nm&}V  
for i = 1:2 2e+UM$  
    for j = 1:2 Si2k"<5U  
        %% 计算第1个传感器新门限值分子与分母的求和部分 pnl{&<$C%C  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); !E<[JM  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); 9vuyv*-}e  
    end GFj{K
 
end n,vct<&z@  
for i = 1:2 x|/|jzJSX  
    for j = 1:2 $O&b``  
        %% 计算第2个传感器新门限值分子与分母的求和部分 N({MP
O9  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); 1OG
x>J6  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); c,np2myd  
    end k3lS8d7  
end |HiE@  
for i = 1:2 1{)5<!9!l  
    for j = 1:2 Q3kdlxXR  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); {2O1"|s ,  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); }MJy +Z8&  
    end Ci@o|Y }tP  
end ,?J!  
if(denominator(1) == 0) f',Op1o  
     break; -^&<Z 0m  
end wcrCEX=I>{  
if(denominator(2) == 0) >8~.wXyoC  
     break; *RI]?j%B  
end LC,F <>w1  
if(denominator(3) == 0) 3
+)J @(a  
     break; ? ^0:3$La  
end qzYwt]GNS  
for i = 1:3 k|e7a2Wwt  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); h9t$Uz^N  
end VACQ+  
if(abs(RB-old_RB) < epsilon) H*d9l2,KZS  
    break; .p<:II:6  
else `l\7+0W  
    old_RB = RB; Kfbb)?  
end }~YA5^VQ$  
if(whileindex == 20) %|s;C  
    break; j@n)kPo,1  
end [`ebM,W  
1T"`vtR  
end )X1{  
result(index+1) = RB; Ot4 Z{mA  
end ef8s<5"4  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 u0JB\)(-/h  
x = 0:0.05:1; 4`4kfi
S$  
plot(x,result,'r'); A=$04<nP8!  
title('贝叶斯风险值-先验概率P0曲线'); v"y-0$M  
xlabel('先验概率P0'); P{8iJ`rBG  
ylabel('贝叶斯风险值'); iFd+2S%  
for index = 0:20                           \{Y 7FC~  
P0 = index/20;                             LK{*sHi$  
P1 = 1 - index/20;                              %计算发送1的先验概率P1 cq,SP&T~
 
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 3atBX5  
%%%计算贝叶斯风险表达式中的相关常量%%% =2`[&  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); DwM)r7<Ex  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); :NhO2L  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); Zd3S:),&  
old_RB = 0; "IZa!eUW  
whileindex = 0; Gj1&tjK  
%%%求解贝叶斯风险值%%% ew>XrT=Zm  
while  (1) ]N~2 .h  
whileindex = whileindex + 1;     RVZ")Z(  
P = zeros(3,2,2); h0(BO*cy  
for i = 1:3 3U<cWl@  
    %%% P(i,j,k)表示，对于第i个传感器，发送k-1，而判决为j-1的概率。%%% S ^!n45l  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf Y4J3-wK5  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   J9\Cm!H  
                                           %计算第i个传感器的漏报概率Pm 1$xNUsD2  
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf aZH:#lUlj  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd "Bh}}!13  
end f/U`  
iW)8j 8  
% 计算融合中心的P(u|H1) /MIe(,>Uh  
Pu_h1 = zeros(2,2,2); Lm iOhx  
for i = 1:2 4-l8,@9  
    for j = 1:2 q_MPju&*  
        for k = 1:2 p\Q5,eg  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           MU($|hwiL  
        end FI)17i$  
    end :">!r.Q  
end =rMUov h  
T(#J_Y  
% 计算融合中心的P(u|H0) R}-(cc%5  
Pu_h0 = zeros(2,2,2); 3OrczJ=[UF  
for i = 1:2 RFi S@.7  
    for j = 1:2 lS"T4 5  
        for k = 1:2 Jf{*PgP  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); =J18eH!]  
        end E~DQ-z  
    end ZJnYIK  
end Df}A^G >
X  
a4mn*,  
% 计算融合中心的P(u0=1|u)% j@AIK+0Qc  
Pu0_1_u = zeros(2,2,2); kDEXN  
for i = 1:2 DEBB()6,  
    for j = 1:2 TEP,Dq  
        for k = 1:2 RF`.xQ26=  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); U&g@.,Y#  
            if(judge < 0) 6O7'!@@  
                Pu0_1_u(i,j,k) = 1; nXaC3W:"  
            else j5(Z_dm'  
                Pu0_1_u(i,j,k) = 0; O\ GEay2  
            end _mj,u64  
        end 034iK[ib"  
    end `}D,5^9]  
end Wvq27YK'  
B?OFe'*  
% 计算融合中心的贝叶斯风险 l#g\X'bK  
RB = C; t=BUN  
for i = 1:2 0%32=k7O[  
    for j = 1:2 !eGC6o}f  
        for k = 1:2 lXx=But  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); ]P9l jwR  
        end ]MqMQLG0t  
    end ]RZ|u*l=x  
end 2A']yD  
EH-sZAv  
% 计算A_ui ^\hG"5#  
for i = 1:2 0
3L]  
    for j = 1:2 %h hf
U6[  
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); B+$%*%b  
    end NyaQI<5D  
end |-b#9JQ[A  
for i = 1:2 t0(A4E  
    for j = 1:2 6gkV*|U,e  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); MH =%-S   
    end df*#!D7oz  
end $r^GE  
for i = 1:2 dhC$W!N7!  
    for j = 1:2 GiJ *Wp  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); k6;?)~.  
    end nB_?ckj,  
end T tfo^ksw  
b}2ED9HG\  
% 计算P(ui|h0) k)i3   
for i = 1:2 55G+;  
    for j = 1:2 [NF'oRRD9s  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); nxO"ua  
    end z$&{:\hj  
end ?3/qz(bM  
for i = 1:2 el&0}`K  
    for j = 1:2 #{6{TFx\  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); %{7|1>8  
    end 'I`&Yo~c9  
end `oAW7q)~  
for i = 1:2 ##q2mm:a9P  
    for j = 1:2 0$(WlP|  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); *-#&K\  
    end H!@kO]?n  
end ):P?  
% 计算P(ui|h1) KsddA  
for i = 1:2 #?RU;1)Cw  
    for j = 1:2 2ElJbN#  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); .fn\]rUv  
    end >;jZa  
end ,D5
cjaX<  
for i = 1:2 m1j*mtu  
    for j = 1:2 gGR"Z]DBk  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); Z EQ@IS:Y  
    end [ieI;OG;  
end Tg7an&#  
for i = 1:2 FX;QG94!  
    for j = 1:2 +*\u :n  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); %9 SJ E  
    end u6J8"< -W  
end y>S.?H:P  
%%%计算新门限值T %%% 2_)\a(.Qu  
numerator = zeros(1,3); {Je[ZQ$  
denominator = zeros(1,3); G5{T5#  
for i = 1:2 ;Y)w@bNt@  
    for j = 1:2 O8f?; ]  
        %% 计算第1个传感器新门限值分子与分母的求和部分 *}Xf!"I#]N  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); K> %Tq  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); CVDV)#JA  
    end x!hh"x  
end <3L5"77G6  
for i = 1:2 ZVW'>M7.  
    for j = 1:2 [RS|gem`  
        %% 计算第2个传感器新门限值分子与分母的求和部分 XUrXnz|>  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); T!uM+6|Y  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); &`h{iK7  
    end !'Ak&j1:`  
end at@G/?  
for i = 1:2 JX<)EZ!F  
    for j = 1:2 Yh7rU?Gj  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); +|*IZ:w)  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); H!c@klD
 
    end "x%Htq@  
end t1]K<>g  
if(denominator(1) == 0) )z>|4@,  
     break; G%BjhpL  
end SP@ >vl+;  
if(denominator(2) == 0) zlyS}x@p  
     break; sXqz+z$*  
end $5>e  
if(denominator(3) == 0) 5b5x!do  
     break; P=qa::A  
end 9q)Kfz  
for i = 1:3 Ii6<b6-  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); GeI-\F7b  
end G3txj  
if(abs(RB-old_RB) < epsilon) qjwxhabc  
    break; ?}qttj  
else 5CuuG<0  
    old_RB = RB; K~uXO  
end #HYr0Tw6`  
if(whileindex == 20) Y&`=jDI  
    break; yZAS#ko}}  
end lVd^ ^T*fh  
u\a#{G;Z  
end W=lyIb{?^0  
result(index+1) = RB; }pDqe;a{  
end toLV4BtIG  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 #||}R[~P"  
x = 0:0.05:1; 1Vsz4P"O $  
plot(x,result,'r'); Y1L[;)Hn  
title('贝叶斯风险值-先验概率P0曲线'); wrviR  
xlabel('先验概率P0'); c>,KZ!  
ylabel('贝叶斯风险值'); ^/uA?h:]\