[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                           #$7d1bx  
P0 = index/20;                             rDFDrviW_  
P1 = 1 - index/20;                              %计算发送1的先验概率P1 BwMi@r =  
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 *"Yz"PK  
%%%计算贝叶斯风险表达式中的相关常量%%% n6D9f~8"  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); t`=TonLb8  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); %EkV-%o*  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); JAJo^}}{b  
old_RB = 0; TbX#K:l  
whileindex = 0; hr3RC+ y  
%%%求解贝叶斯风险值%%% v zgR3r  
while  (1) 6-#<*Pg  
whileindex = whileindex + 1;     q-gp;Fm  
P = zeros(3,2,2); tmT/4Ia  
for i = 1:3 lz=$Dz  
    %%% 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。%%% HwfBbWHr'  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf 29{Ep  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   ?#~3%$>  
                                           %计算第i个传感器的漏报概率Pm ~2u~}v5m7  
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf Ey<vvZ  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd "2}{lu  
end ln4gkm<]t  
dd1CuOd6(1  
% 计算融合中心的P(u|H1) uc;1{[5`1q  
Pu_h1 = zeros(2,2,2); #tX\m;  
for i = 1:2 F]o&m::/K  
    for j = 1:2 %N+8K  
        for k = 1:2 '-C%?*ku  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           u
~SvR~OE  
        end Age  
    end )y>o;^5
'  
end 6[ }~m\cY  
XgRrJ.  
% 计算融合中心的P(u|H0) 7Nx5n
<  
Pu_h0 = zeros(2,2,2); 6[3oOO:uo  
for i = 1:2 RW| LL@r  
    for j = 1:2 eaLR-+vEB  
        for k = 1:2 RhwqAok|lj  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); /g$cQ=c  
        end );EW(7KeL  
    end Mbbgsy3W  
end
n^vL9n_N  
|dNJx<-  
% 计算融合中心的P(u0=1|u)% PNy)TqdRS  
Pu0_1_u = zeros(2,2,2); LpRl!\FY$  
for i = 1:2 12l-NWXf  
    for j = 1:2 ab"6]%_  
        for k = 1:2  uP|Py.+  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); ueI1O/Mi  
            if(judge < 0) 6&,n\EXF  
                Pu0_1_u(i,j,k) = 1; 
D<=x<.  
            else ZqT8G  
                Pu0_1_u(i,j,k) = 0; u /PaXQ  
            end =AEl:SY+  
        end @c3GJ'"X  
    end 'dJ#NT25  
end DD 8uG`<  
^na8d's:  
% 计算融合中心的贝叶斯风险 7G6XK   
RB = C; uu0"k<Tp  
for i = 1:2 i,#j@R@.C7  
    for j = 1:2 y[QQopy4:  
        for k = 1:2 `y"(\1  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); 2J7= O^$?  
        end q8&2
M  
    end `zf,$67>1  
end > mI1wV[  
%#5\^4$z|N  
% 计算A_ui Pn?Ujjv  
for i = 1:2 R(YhVW_l  
    for j = 1:2 K>
%}m,  
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); H}v.0R  
    end tYb
8a  
end R`M>w MLH  
for i = 1:2 M"XILNV-~  
    for j = 1:2 ]M^k~Xa  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); 5Bwr\]%$P  
    end 3PRg/vD3  
end iYbp^iVg  
for i = 1:2 5:S=gARz  
    for j = 1:2 b!(ew`Y;  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); ^eF%4DUC;  
    end t<8vgdD  
end $y%X#:eLJ  
`Wc"Ix0  
% 计算P(ui|h0) Vo1,{"k  
for i = 1:2 pF0sXvWGG  
    for j = 1:2 RP!!6A6:  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); h?t#ABsVK  
    end JSXJlau  
end M1nH!A~o  
for i = 1:2 cV;<!f+  
    for j = 1:2 0}LBnV  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); Ih Yso7g  
    end 9)c{L<o}T  
end hA?Flq2QV  
for i = 1:2 +M )ep\j  
    for j = 1:2 6t zUp/O  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); *[VO03  
    end |l\!  
end 6yn34'yw  
% 计算P(ui|h1) VkFvV><"  
for i = 1:2 ub/Z'!  
    for j = 1:2 .\Z/j  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); &Tc:WD  
    end U%.%:'eV=  
end l]g /rs  
for i = 1:2 h=?V)WSM  
    for j = 1:2 x}^:Bs+j  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); g5",jTn#  
    end @*Y"[\"$  
end JAt$WW{  
for i = 1:2 mGZJ$|  
    for j = 1:2 &#[w*t(A  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); $] ])FM"b  
    end m-xnbTcQ  
end c>SFttbU  
%%%计算新门限值T %%% /#<R  
numerator = zeros(1,3); N@qP}/}8  
denominator = zeros(1,3); .qd/ft2  
for i = 1:2 uUhqj.::<Y  
    for j = 1:2 E&;[E  
        %% 计算第1个传感器新门限值分子与分母的求和部分 9F~e^v]zp  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); T[?wbYfW  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); 9_=0:GHk  
    end cd&^ vQL8  
end JD\yl[ac%  
for i = 1:2 u& 4i=K'x8  
    for j = 1:2 MWGs:tpL4  
        %% 计算第2个传感器新门限值分子与分母的求和部分 4n9".UH
h  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); !O*'mX  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); iX&eQ{LB  
    end f`;y "ba  
end kjj4%0"  
for i = 1:2 ]VKM3[   
    for j = 1:2 OM>,1;UH]  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); a*hWODYn  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); A{Kc"s4fO  
    end  dmR>u
 
end `s )- lI  
if(denominator(1) == 0) |\}&mBR  
     break; Ym% $!#  
end E{wnhsl{  
if(denominator(2) == 0) :D|5E
>o(  
     break; ^uWPbW&/q  
end l+ ,p=  
if(denominator(3) == 0) zh.^> `   
     break; 6%-RKQi  
end KF.O>c87&  
for i = 1:3 xM+_rU M|h  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); 24g\xNnt  
end $a@T:zfe  
if(abs(RB-old_RB) < epsilon) \X*Es.;|x  
    break; nE
&`~  
else |>Ld'\i8  
    old_RB = RB; 9mmkFaBQ  
end dCb7sqJ%  
if(whileindex == 20) ~vbyX  
    break; (yJY/|  
end ]NEr]sc-"F  
04j]W]8#  
end ~|:U"w\[=  
result(index+1) = RB; -n:
~m p  
end '9ki~jtf=  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 -$ VP#%  
x = 0:0.05:1; .S_7R/2(?  
plot(x,result,'r'); MQ#nP_i  
title('贝叶斯风险值-先验概率P0曲线'); u?Uu>9@Z  
xlabel('先验概率P0'); 2iWSk6%R  
ylabel('贝叶斯风险值'); |#b]e|aP  
for index = 0:20                           =K\xE"  
P0 = index/20;                             IgmCZ?l&0  
P1 = 1 - index/20;                              %计算发送1的先验概率P1 `MLOf  
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 `iQ])C^d  
%%%计算贝叶斯风险表达式中的相关常量%%% B,5kG{2!  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); 6*aU^#Hz6  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); g7UZtpLTm  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); G(3wI}  
old_RB = 0; /g`!Zn8a  
whileindex = 0; sk%Xf,  
%%%求解贝叶斯风险值%%% 3>'TYXs-  
while  (1) R9&3QRW|  
whileindex = whileindex + 1;     b)[2t^zG  
P = zeros(3,2,2); [yhK4A  
for i = 1:3 De-hHY{>  
    %%% 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。%%% Bs3M7zRG  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf w-j^jU><3  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   {i^F4A@=Z  
                                           %计算第i个传感器的漏报概率Pm ^\f1zg9I  
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf u\AL`'v  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd ymW? <\AD,  
end  5(\H:g\z  
zk;'`@7  
% 计算融合中心的P(u|H1) 5r` x\  
Pu_h1 = zeros(2,2,2); f=EWr8mno  
for i = 1:2 sd5)We  
    for j = 1:2 X T<SR]  
        for k = 1:2 ~Fe$/*v  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           5%jy7)8C  
        end ?onEqH>  
    end D#k ~lEPub  
end FX  %(<M  
J*Q+$Ai~  
% 计算融合中心的P(u|H0) c;B:o  
Pu_h0 = zeros(2,2,2); e~ZxDAd  
for i = 1:2 9_b_O T  
    for j = 1:2 hh[@q
*C  
        for k = 1:2 <\'aUfF v  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); ?u4t;  
        end |V&E q>G  
    end V<i_YLYmJe  
end Y2TXWl,Jk  
3Fg{?C_l  
% 计算融合中心的P(u0=1|u)% Yh["IhjR  
Pu0_1_u = zeros(2,2,2); 47=YP0r?>T  
for i = 1:2 @$|8zPs  
    for j = 1:2 g7;O
Z#\  
        for k = 1:2 ( }RJW:  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); ZVyJ%"(E  
            if(judge < 0) so>jz@!EE  
                Pu0_1_u(i,j,k) = 1; 7PW7&]-WQ  
            else tuslkOE#  
                Pu0_1_u(i,j,k) = 0; VvUP;o&/  
            end rU |%  
        end u*m|o8  
    end re xMS  
end N[zR%(YS  
YD,<]q%  
% 计算融合中心的贝叶斯风险 0O!A8FA0  
RB = C; B;^1W{%J  
for i = 1:2 7$JOIsM  
    for j = 1:2 |%g)H,6c  
        for k = 1:2 RgD%pNhI  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); xdgbs-a)  
        end LL_@nvu}M  
    end 5D <  
end %D49A-R  
.Q!pQ"5  
% 计算A_ui }F';"ybrU)  
for i = 1:2
Ms=N+e$n  
    for j = 1:2 UZ;FrQ(l{  
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); }a"
koL  
    end hEA;5-m  
end v:gdG|n"  
for i = 1:2 ^Z+p_;J$p  
    for j = 1:2 Sw.Kl 0M  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); Kw =RqF  
    end Rr0]~2R  
end 9yK\<6}}QH  
for i = 1:2 717OzrF}A?  
    for j = 1:2 ~hb;kc3  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); 8xt8kf*k  
    end Se.qft?D%(  
end = G>Y9Sc  
=bOMtQ]  
% 计算P(ui|h0) c{3P|O&.  
for i = 1:2 0O?\0k;o  
    for j = 1:2 qV)hCc/ ~  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); AbL(F#{  
    end u)[i'ceQZ:  
end RN2z/FUf  
for i = 1:2 bHg 0,N  
    for j = 1:2 wWVB'MRXB,  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); Rxq4Diq5k  
    end %x8vvcO^t  
end IqFmJs|C  
for i = 1:2 6t{G{ ]  
    for j = 1:2
AHzm9U @  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); mYFc53B  
    end ?!u9=??  
end ]zz%gZz  
% 计算P(ui|h1) -HvJ&O.V$  
for i = 1:2 }\QXPU{UVd  
    for j = 1:2 JYnyo$m/  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); Ie}7#>S  
    end >?jmeD3u  
end }vd72PB  
for i = 1:2 v)aV(Oa  
    for j = 1:2 0E7h+]bh|  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); e\._M$l  
    end @o6!  
end v.53fx  
for i = 1:2 XPLm`Q|1#t  
    for j = 1:2 EY@KWs3"H  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); xD9ZL  
    end 3$3
%W<&^  
end YbF}>1/"  
%%%计算新门限值T %%% BKK@_B"  
numerator = zeros(1,3); ;;N#'.xD  
denominator = zeros(1,3); }O\g<ke:u  
for i = 1:2 b
lUS6"kV}  
    for j = 1:2 qOAhBZ~  
        %% 计算第1个传感器新门限值分子与分母的求和部分 NNBT.k3)  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); y]g
5S-G  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); "iJAM`Hi  
    end Pf~0JNnc  
end } x KvN  
for i = 1:2 TVVu_ib  
    for j = 1:2 xLP8*lvy  
        %% 计算第2个传感器新门限值分子与分母的求和部分 gZ us}U  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); MhjIE<OI=  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); W~5gTiBZ]  
    end =N2@H5+7  
end tm.&k6%  
for i = 1:2 & j*Ylj}  
    for j = 1:2 tILnD1q  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); S[CWrPaDQ  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); hyY^$p+  
    end ~FVbL-2  
end | Pqs)Mb]  
if(denominator(1) == 0) f\;f&GI  
     break; ^97[(89G9  
end w+{{4<+cd  
if(denominator(2) == 0) 0zk054F'  
     break; 93/`e}P"o  
end Yc5<Y-W  
if(denominator(3) == 0) f[q_eY  
     break; }tJMnq/m($  
end rS0#]Gg  
for i = 1:3 -|
P7e  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); r;O?`~2'4  
end Ch]q:o4  
if(abs(RB-old_RB) < epsilon) <9x|)2P  
    break; EcPvE=^c  
else P0rdGf 5T  
    old_RB = RB; 88}04  
end G+WCE*  
if(whileindex == 20) ;L,yJ~  
    break; KP!7hJhw  
end
UMH~Q`"  
&zPM#Q  
end z=4E#y`?U  
result(index+1) = RB; 'cY@Dqg1  
end ?sxf_0*  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 m|[cEZxHB  
x = 0:0.05:1; +!t *LSF  
plot(x,result,'r'); *7qa]i^]  
title('贝叶斯风险值-先验概率P0曲线'); Xy9'JVV6  
xlabel('先验概率P0'); n.A*(@noe  
ylabel('贝叶斯风险值'); {"0n^
!  
for index = 0:20                           f5R%F~
 
P0 = index/20;                             _+gpdQq\p  
P1 = 1 - index/20;                              %计算发送1的先验概率P1 ~]BR(n  
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 xEB4oQ5  
%%%计算贝叶斯风险表达式中的相关常量%%% 9lX[rBZ  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); :(I=z6  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); NM1TFs2Y*  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); akQb%Wq  
old_RB = 0; mG%cE(j*D  
whileindex = 0; |[!0ry*N%  
%%%求解贝叶斯风险值%%% cGWL'r)P  
while  (1) 
<JZa  
whileindex = whileindex + 1;     Y'y$k  
P = zeros(3,2,2); z.W1Za  
for i = 1:3 G~NhBA9  
    %%% P(i,j,k)表示，对于第i个传感器，发送k-1，而判决为j-1的概率。%%% `KE(R8y  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf V{{UsEVO  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   XX*f  
                                           %计算第i个传感器的漏报概率Pm cSj(u%9}  
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf ! &
V,+}>)  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd VKi3z%kwK  
end >Lz2zlZI  
r
?x~`C  
% 计算融合中心的P(u|H1) !zxq9IhWR  
Pu_h1 = zeros(2,2,2); XlGB`P>?KD  
for i = 1:2 gIcPKj"8${  
    for j = 1:2 (; Zl  
        for k = 1:2 Kt_HJ!  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           obw:@
i#  
        end 6,]2;
'  
    end |h:3BV_  
end +*RpOtss  
7.C]ZcU  
% 计算融合中心的P(u|H0) !Tu.A@  
Pu_h0 = zeros(2,2,2); & aF'IJC  
for i = 1:2 FFH{#|_1  
    for j = 1:2 hflDVG
BW  
        for k = 1:2 ) |hHbD^V  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); 'eoI~*}3WQ  
        end C,u;l~zz  
    end qche7kg!a  
end uMBb=   
Pv@;)s(-  
% 计算融合中心的P(u0=1|u)% dRTpG
z  
Pu0_1_u = zeros(2,2,2); !" : arK  
for i = 1:2 H/ub=,Ej*  
    for j = 1:2 m>b i$Y  
        for k = 1:2 +Jc-9Ko\c;  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); s_,&"->  
            if(judge < 0) YGLR%PYv"  
                Pu0_1_u(i,j,k) = 1; B^1Io9  
            else H{;8i7%  
                Pu0_1_u(i,j,k) = 0; gwYTOs^  
            end UOIZ8Po  
        end /h@rLJ)o>  
    end tWdP5vfp  
end wSs78c=  
St1>J.k_  
% 计算融合中心的贝叶斯风险 y] ~X{v  
RB = C; N?Ss/by8Sg  
for i = 1:2 l1RFn,Tzr  
    for j = 1:2 ;}k_2mr~  
        for k = 1:2 3*b!]^d:D  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); j0jam:.p  
        end ix}*whW=U  
    end \y/+H  
end J~G"D-l<9/  
g/,O51f'  
% 计算A_ui O0"&wvR+5  
for i = 1:2 c>Ljv('bj  
    for j = 1:2 $E@ke:  
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); fGLOXbsA  
    end v aaZ  
end yM34GS=,J  
for i = 1:2 t,;b*ZR  
    for j = 1:2 u"a$/  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); 2qkC{klC^M  
    end Q_a%$a.rV  
end wmPpE_{  
for i = 1:2 Iyvl6  
    for j = 1:2  ]cI(||x  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); U0S}O(Ptr  
    end 9a_(_g>S  
end O4 Y;  
0 .p $q  
% 计算P(ui|h0) gClDVO  
for i = 1:2 fmq^AnKd  
    for j = 1:2 on1mu't_;  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); BcoE&I?[m|  
    end Xq%!(YD|  
end YuDNm}r[  
for i = 1:2 4^B:Q9B)  
    for j = 1:2 k4%> F  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); /h%MWCZWm^  
    end v6?<)M%  
end gM3gc;  
for i = 1:2 }(XvI^K[^  
    for j = 1:2 ^SRa!8z$W  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); Jh:-<xy)  
    end v]27+/a$c  
end ("BFI  
% 计算P(ui|h1) L9U<E $%#  
for i = 1:2 R:JS)>B  
    for j = 1:2 _'oy C(:}  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); y/2U:H  
    end +NEP*mk  
end I!Za2?  
for i = 1:2 k07) g:_  
    for j = 1:2 h Tn^:%(  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); k .l,>s`!  
    end P6 G/J-  
end )+9D$m=P;  
for i = 1:2 >Y< y]vM:  
    for j = 1:2 3/@'tLtN  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); JGD{cr[S  
    end zR3Z(^]v  
end efP2C\  
%%%计算新门限值T %%% Qi7^z;  
numerator = zeros(1,3); 7+u%]D!  
denominator = zeros(1,3); }Mo9r4}  
for i = 1:2 ^ihXM]1{G  
    for j = 1:2 Ic&t_B*i}]  
        %% 计算第1个传感器新门限值分子与分母的求和部分 73(T+6`  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); \9k{"4jX\  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); cw<DM%p  
    end 3B"rI  
end ikRIL2Y  
for i = 1:2 .< vg[  
    for j = 1:2 7\U1K
^q  
        %% 计算第2个传感器新门限值分子与分母的求和部分 /ADxHw`k  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); C5RDP~au  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); hOMFDfhU  
    end o-Idr{  
end z?"5="D  
for i = 1:2 JT^E`<nn  
    for j = 1:2 r5iO%JFg  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); |:r
/K  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); 0I?3@Nz6  
    end Azz]TO  
end gkk<-j'  
if(denominator(1) == 0) D&9j$#9Rh  
     break; TzL40="F  
end pr0V)C6  
if(denominator(2) == 0)

_zmx
  
     break; 6l vx  
end JkxS1  
if(denominator(3) == 0) v V^GIWK  
     break; khv!\^&DD  
end iK%Rq  
for i = 1:3 |PJW
2PN  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); Jp-ae0 Ewa  
end mQs'2Y6Oa  
if(abs(RB-old_RB) < epsilon) n"K7@[d  
    break; OEwfNZQ-  
else UXk8nH  
    old_RB = RB; F<(xz=  
end IfXLnD^||  
if(whileindex == 20) :M[E-j;  
    break; V!U[N.&$  
end f|^f^Hu:{  
C aJ
D*  
end 4QZy-a*tA  
result(index+1) = RB; wD,F=O  
end ^:)&KV8D|  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 l*:p==  
x = 0:0.05:1; "^D6%I#T  
plot(x,result,'r'); YKc{P"'/|  
title('贝叶斯风险值-先验概率P0曲线'); VD3[ko  
xlabel('先验概率P0'); }t-r:R$,  
ylabel('贝叶斯风险值'); &s<