[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                           ['}|#
3*w  
P0 = index/20;                             3<r7"/5  
P1 = 1 - index/20;                              %计算发送1的先验概率P1 ]
AY 4bm  
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 })-V,\  
%%%计算贝叶斯风险表达式中的相关常量%%% g9pKoi|\E  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); );.$`0  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); ! *sXLlS  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); hH1Q:}a  
old_RB = 0; Pw<?Dw]m  
whileindex = 0; ~JL
qh  
%%%求解贝叶斯风险值%%% uQO5GDuK>  
while  (1) J -z.  
whileindex = whileindex + 1;     >`x|E-X"  
P = zeros(3,2,2); 89&9VX^A  
for i = 1:3 }U_^zQfaj  
    %%% 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。%%% 7#E/Q~]'6  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf ElW\;C:K*  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   #7O7O~  
                                           %计算第i个传感器的漏报概率Pm ]vQa~}  
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf %HG+|)b  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd fS4foMI63)  
end "rn  
s=jmvvs_V}  
% 计算融合中心的P(u|H1) zq</(5H  
Pu_h1 = zeros(2,2,2); %>*?uO`z[  
for i = 1:2 fYP,V0P  
    for j = 1:2 6-wpR  
        for k = 1:2 NRx 7S9W  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           v"1&xe^4  
        end 5_!L"sJ  
    end \7LL neq  
end -KGJr  
F`:Q  
% 计算融合中心的P(u|H0) 7I[[S!((s  
Pu_h0 = zeros(2,2,2); m-O*t$6  
for i = 1:2 N9/k`ZGC  
    for j = 1:2 ">Qxb.Y}  
        for k = 1:2 0OHXg=  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); s1_Y~<yX  
        end RkG?R3e  
    end bW W!,-|R  
end >k"O3Pc@  
g&g:HH:  
% 计算融合中心的P(u0=1|u)% d8: $ll  
Pu0_1_u = zeros(2,2,2); QgU]3`z"  
for i = 1:2 EcH
Zmf  
    for j = 1:2 w@-G_-6W  
        for k = 1:2 epnZGz,A  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); VjWJx^ZL#  
            if(judge < 0) ELwXp|L  
                Pu0_1_u(i,j,k) = 1; uY~mi9E  
            else HMUx/M.j  
                Pu0_1_u(i,j,k) = 0; _ooHB>sH  
            end eW8cI)wU  
        end ]&]G  
    end {Ycgq%1>]  
end DLbP$&o  
Lmjd,t  
% 计算融合中心的贝叶斯风险 @M\JzV4 A[  
RB = C; VDnrm*  
for i = 1:2 hD5@PeLh  
    for j = 1:2 }` 3-  
        for k = 1:2 _W(xO |,M  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); DL,R~  
        end 1^$hbRq  
    end X]}ai5  
end -"xAeI1+  
^IiA(?8  
% 计算A_ui %@:>hQ2;  
for i = 1:2 %t&Lq}e  
    for j = 1:2 _(f@b1O~
 
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); |gA@$1+}  
    end <.Tllk@r)  
end &;ddnxFI  
for i = 1:2 2Di~}*9&  
    for j = 1:2 8x1!15Wiz  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); mfZbo#KS#v  
    end =M 8Mt/P  
end s&ox%L4  
for i = 1:2 Kc]cJ`P4.  
    for j = 1:2 uO1^Q;F  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); ;0WAfu}#H  
    end k`>qb8,  
end dwB#k$VIOw  
yam'LF  
% 计算P(ui|h0) ?3N/
#  
for i = 1:2 9'p| [?]v  
    for j = 1:2 \Nik`v*Pd  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); i-0 :Fs  
    end x%X3FbF]  
end 2f:hz  
for i = 1:2 R~oY R,L;  
    for j = 1:2 4mY^pQ1=L  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); g[i;>XyP  
    end G--vwvL  
end 1R-0b{w[  
for i = 1:2 <tTNtBb  
    for j = 1:2 MMa`}wSs  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); @GAj%MK$  
    end Lb$Uba-_  
end 53uptQ{  
% 计算P(ui|h1) `GqF/?i  
for i = 1:2 PYQ  
    for j = 1:2 1N _"Mm{  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); MDa[bQNM  
    end X{xkXg8h  
end FsUH/Y y  
for i = 1:2 xy>$^/[$  
    for j = 1:2 0*:n<T9  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); =z5=?  
    end Z=
-#{{bv  
end >%6j-:S  
for i = 1:2 %+/v")8+?  
    for j = 1:2 D`n<!"xg@$  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); {!( htg;  
    end `ci  P  
end N/-(~r[
  
%%%计算新门限值T %%% W9gQho%9b  
numerator = zeros(1,3); <8
%+-[(  
denominator = zeros(1,3); mu0L_u(P  
for i = 1:2 ~jKIuO/  
    for j = 1:2 K'8o'S_bF  
        %% 计算第1个传感器新门限值分子与分母的求和部分 bL<H$DB6  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); %zc.b  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); MV<)qa T  
    end OtL~NTY  
end Y]R=z*i%  
for i = 1:2 @I Y<i5(  
    for j = 1:2 b^i$2$9_  
        %% 计算第2个传感器新门限值分子与分母的求和部分 n)cc\JPQ  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); ?}^ y6  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); :6C R~p  
    end b?=r%D->w  
end vyc<RjS_x  
for i = 1:2 s7E %Et  
    for j = 1:2 miBCq
l@x  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); "c~``i\G  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); .))k
  
    end U'b}%[  
end m?Jnb\0  
if(denominator(1) == 0) or ~@!  
     break; M&zB&Ia"'  
end dG3?(}p+  
if(denominator(2) == 0) hDJ+Rk@  
     break; _j$V[=kdM/  
end i]zh8|">  
if(denominator(3) == 0) jMWTNZ  
     break; 5Mxl({oI]  
end 0*^f EoV  
for i = 1:3 LVP6vs  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); s!0
9cS  
end h Q Att  
if(abs(RB-old_RB) < epsilon) 5Q}HLjG8Z  
    break; zGO_S\  
else F9w2+z.  
    old_RB = RB; MAqETjB  
end Lo'G
fHE  
if(whileindex == 20) (q7;/n  
    break; WZA1nzRc  
end E]{0lG`l  
[PrJf"Z "  
end *s1o?'e  
result(index+1) = RB; N+R{&v7=F%  
end '
#K~hep  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 ZnbpIJ8cV  
x = 0:0.05:1; zxv y&  
plot(x,result,'r'); L(y~ ,Kc  
title('贝叶斯风险值-先验概率P0曲线'); K~&3etQF  
xlabel('先验概率P0'); ,9:v2=C_  
ylabel('贝叶斯风险值'); |<]wM(GxE  
for index = 0:20                           |a1zJ_t4  
P0 = index/20;                             Q\~#cLJ/  
P1 = 1 - index/20;                              %计算发送1的先验概率P1
-K^(L#G  
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 $ ga,$G  
%%%计算贝叶斯风险表达式中的相关常量%%% <1sUK4nQ,  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); qsN}KgTjg  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); GYT0zMMf  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); y}QqS/  
old_RB = 0; ;rh=63g  
whileindex = 0; kyB>]2  
%%%求解贝叶斯风险值%%% >hnhV6ss  
while  (1) ^s8JW"H  
whileindex = whileindex + 1;     @ bvWqMa  
P = zeros(3,2,2); VF-[O  
for i = 1:3 5=poe@1g  
    %%% 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。%%% }sZy|dd  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf }/xdHt  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   4mGRk)hk:>  
                                           %计算第i个传感器的漏报概率Pm z1e+Ob&  
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf W.7u6F`  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd $H,9GIivD  
end ~8Ef`zL  
0,LUi*10  
% 计算融合中心的P(u|H1) g[{rX4~|  
Pu_h1 = zeros(2,2,2); IxlPpS9Wx  
for i = 1:2 jN+2+P%OL  
    for j = 1:2 up3mum  
        for k = 1:2 TTxSl p2=;  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           78mJ3/?rC  
        end ^> d"D  
    end v<} $d.&*  
end Df$Yn  
>vf
LlYx  
% 计算融合中心的P(u|H0) H
Ba6Y&)<  
Pu_h0 = zeros(2,2,2); w/0;N`YB  
for i = 1:2 a'G[!"  
    for j = 1:2 1kc{`oL  
        for k = 1:2 Z!?T&:  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); e $5s],,n  
        end j1@PfKh  
    end ciPaCrV  
end RCGpZyl  
dfeN_0`-  
% 计算融合中心的P(u0=1|u)% ,+_gx.H2j  
Pu0_1_u = zeros(2,2,2); %+$!ctn  
for i = 1:2 61K:SXj  
    for j = 1:2 \cAifU  
        for k = 1:2 ^
F"eHUg  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); Zvz}Z8jW  
            if(judge < 0) 3t]0  
                Pu0_1_u(i,j,k) = 1; 4,6?sTuX  
            else >F!X'#Iv  
                Pu0_1_u(i,j,k) = 0; `? f sU  
            end na/,1iI<  
        end OdJ=4 x>  
    end du=[r  
end X0m6<q  
wlX K2D  
% 计算融合中心的贝叶斯风险 x A ZRl  
RB = C; P$A'WEO'  
for i = 1:2 `MMZR=LA  
    for j = 1:2 6}mSA@4&  
        for k = 1:2 hcD.-(-;)  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); HtI>rj/\ x  
        end wMiRN2\^  
    end B{_-k  
end #fe zUU  
Q mz3GH@wg  
% 计算A_ui ~!dO2\X+  
for i = 1:2
6FI`0j=~  
    for j = 1:2 i ;YRE&X  
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); w>#.id[k  
    end ,6\oT;G  
end y=WCR*N  
for i = 1:2 EO.}{1m=hx  
    for j = 1:2 nqujT8  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); gG6BEsGa,  
    end W QyMM@#  
end cy_zEJjbD  
for i = 1:2 V_Y2@4  
    for j = 1:2 /%)x!dmy  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); A `=.F  
    end Ils
^t  
end U| 1&=8l
 
ZP7wS
  
% 计算P(ui|h0) }D#[yE,=\  
for i = 1:2 x@"`KiEUs  
    for j = 1:2 8F`799[p  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); 6~(
iLtd#  
    end Bdf]?s[]  
end *&yt;|y  
for i = 1:2 1A
9
Gf  
    for j = 1:2 2W`<P2IA  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); >:U{o!N`#_  
    end o7 X5{  
end WPNvZg9*c  
for i = 1:2 y +c 3#  
    for j = 1:2 LfS]m>>e  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); PxZMH=  
    end kv/mqKVr  
end WO6;K]  
% 计算P(ui|h1) X=b]Whuv  
for i = 1:2 []eZO_o6j  
    for j = 1:2 @$d_JwI  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); c"@,|wCUi  
    end V}"w8i+D?  
end V_7QWIdiy>  
for i = 1:2 *6*/kV?F  
    for j = 1:2 x\XOtjJr  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); X6c['Zrc  
    end N]@e7P'9F  
end \wo'XF3:  
for i = 1:2 ,1-#Z"~c  
    for j = 1:2 bG9$&,  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); !F*CEcB  
    end -qnd
BS  
end |zSoA=7?  
%%%计算新门限值T %%% <)9E.h  
numerator = zeros(1,3); i/WiSwh:  
denominator = zeros(1,3); ?f[U8S}  
for i = 1:2 %Nm69j-5%  
    for j = 1:2 l7=WO#Pb  
        %% 计算第1个传感器新门限值分子与分母的求和部分 3P2L phW  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); < {dV=  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); (&Z`P  
    end 5U&?P  
end l
vZ:Aw r  
for i = 1:2 W_N!f=HW  
    for j = 1:2 ) bGzsb1\  
        %% 计算第2个传感器新门限值分子与分母的求和部分 ^c]lEo  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); '@+a]kCMev  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); zd3%9rj$  
    end 'gwh:8Xc  
end *l4`2eqZ  
for i = 1:2 #AUz.WHD  
    for j = 1:2 e!~x-P5M`  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); B&)o:P7h  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); wN
m~H  
    end u~a@:D/F{G  
end #>\+6W17U  
if(denominator(1) == 0) ,t_Fo-i7vI  
     break; `JL&x|q o  
end Arb-,[kwN  
if(denominator(2) == 0) '
fm}&0  
     break; q+-Bl  
end /_*L8b  
if(denominator(3) == 0) m`lsUN,  
     break; u0& dDZ  
end C %o^
AR  
for i = 1:3 DR6 OR B7  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); {n>W8sN<  
end @z)_m!yV1  
if(abs(RB-old_RB) < epsilon) L>EC^2\  
    break; i.`RQZ$,/  
else UA}oOteG  
    old_RB = RB; (Q09$  
end (
)v{HBi  
if(whileindex == 20) \;smH;m  
    break; LAwAFma>  
end c,I|O' &k  
9fL48
f$  
end p#95Q  
result(index+1) = RB; .AgD`wba  
end _n+./B  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 q9&d24|  
x = 0:0.05:1; ;eeu 9_$  
plot(x,result,'r'); ~?)ST?&  
title('贝叶斯风险值-先验概率P0曲线'); ;ywQk| r  
xlabel('先验概率P0'); #I.Wmfz  
ylabel('贝叶斯风险值'); UF00K1dbz  
for index = 0:20                           (7g1e
EK%  
P0 = index/20;                             R\wG3Oxol  
P1 = 1 - index/20;                              %计算发送1的先验概率P1 \;G97o  
T = [1.55 1.5 1.65];                            %初始化3个传感器的判决门限 7 n=fB#!*3  
%%%计算贝叶斯风险表达式中的相关常量%%% UE9r1g`z  
C = P0*cost_fac(1,1) + P1*cost_fac(2,1); Ll L8Q  
C_F = P0*(cost_fac(1,2) - cost_fac(1,1)); LMvsYc~]q  
C_D = P1*(cost_fac(2,1) - cost_fac(2,2)); :2fz4n0{/  
old_RB = 0; M6b; DQ  
whileindex = 0; y$e'-v  
%%%求解贝叶斯风险值%%% hD:$Sv/H  
while  (1) fXHNm$"n  
whileindex = whileindex + 1;     A;A>Q`JJF  
P = zeros(3,2,2); jreY'y:  
for i = 1:3 CdRgI^5  
    %%% P(i,j,k)表示，对于第i个传感器，发送k-1，而判决为j-1的概率。%%% CL$mK5u  
    P(i,2,1) = qfunc(T(i)/sigma(i));       %计算第i个传感器的虚警概率Pf pPdOwK#  
    P(i,1,2) = 1 - qfunc((T(i)-Value(i))/sigma(i));   ,4wZ/r> d  
                                           %计算第i个传感器的漏报概率Pm -IB~lw
 
    P(i,1,1) = 1 - P(i,2,1);               %计算第i个传感器的概率1-Pf >@BvyZ)i  
    P(i,2,2) = 1 - P(i,1,2);               %计算第i个传感器的检测概率Pd &v^LxLt+s  
end L@{5:#-  
Sgt@G=_o  
% 计算融合中心的P(u|H1) 85fBKpEe  
Pu_h1 = zeros(2,2,2); QDC]g.x  
for i = 1:2 xv{iWJcs  
    for j = 1:2 *?`:=  
        for k = 1:2 &\0`\#R  
            Pu_h1(i,j,k) = P(1,i,2)*P(2,j,2)*P(3,k,2);           T?+xx^wYk  
        end QxmVImn"  
    end i}5 #n

 
end 3'WS6B+  
v*nX  
% 计算融合中心的P(u|H0) q)uq?sZe  
Pu_h0 = zeros(2,2,2); <eq93  
for i = 1:2 [yF4_UoF  
    for j = 1:2 v?7.)2XcX  
        for k = 1:2 J8x>vC  
            Pu_h0(i,j,k) = P(1,i,1)*P(2,j,1)*P(3,k,1); S/ Y1NH  
        end hfGA7P"  
    end .hCOi<wB  
end U9KnW]O%"  
WJ+<&6W8  
% 计算融合中心的P(u0=1|u)% nO~TW  
Pu0_1_u = zeros(2,2,2); &zF1&J58z  
for i = 1:2 gn? ~y`  
    for j = 1:2 46dh@&U  
        for k = 1:2 ;TK:D=p4  
            judge = C_F*Pu_h0(i,j,k) - C_D*Pu_h1(i,j,k); PQ2u R  
            if(judge < 0) #n'tpp~O  
                Pu0_1_u(i,j,k) = 1; l*]L=rC  
            else q lL6wzq,  
                Pu0_1_u(i,j,k) = 0; b7/1]  
            end v|XEC[F  
        end uSs~P%@6|  
    end QMzBx*g(  
end ^6~CA
 
G!54 e  
% 计算融合中心的贝叶斯风险 TCSm#?[B  
RB = C; H!ZPP8]j>  
for i = 1:2 5s>$  
    for j = 1:2  [;D4,@A  
        for k = 1:2 m#'2 3  
            RB = RB + Pu0_1_u(i,j,k)*(C_F*Pu_h0(i,j,k)-C_D*Pu_h1(i,j,k)); CW Y'
q  
        end JeJ
c(e  
    end 'W~O?  
end nJYcC"f  
xOjCF&W  
% 计算A_ui Mp~E$f  
for i = 1:2 ]OoqU-q  
    for j = 1:2 mB$r>G/
'  
        A_u1(i,j) = Pu0_1_u(2,i,j) - Pu0_1_u(1,i,j); |/g\N,]  
    end -,~n|ceI  
end hIw<gb4J%  
for i = 1:2 j"E_nV:Qc  
    for j = 1:2 .(  vS/  
        A_u2(i,j) = Pu0_1_u(i,2,j) - Pu0_1_u(i,1,j); 1X/ q7lR  
    end e/WR\B'1  
end ?<Dinq  
for i = 1:2 ;fhFv&`mE  
    for j = 1:2 >sP-)ZeuU[  
        A_u3(i,j) = Pu0_1_u(i,j,2) - Pu0_1_u(i,j,1); ztG_::QtG]  
    end N"b>]Ab] ;  
end 4 ~17s`+  
#{ M$%l>  
% 计算P(ui|h0) B_[^<2_  
for i = 1:2 GCx]VN3&  
    for j = 1:2 'hL\xf{  
        P_u1_h0(i,j) = P(2,i,1)*P(3,j,1); EeKEw Sg  
    end !9i,V{$c`"  
end Pb.-Z@  
for i = 1:2 A8OV3h6]  
    for j = 1:2 S5'BXE,  
        P_u2_h0(i,j) = P(1,i,1)*P(3,j,1); Rtn.cSd  
    end W:P4XwR{  
end Cl]E rg  
for i = 1:2 lhx6+w  
    for j = 1:2 L^VG?J  
        P_u3_h0(i,j) = P(1,i,1)*P(2,j,1); ~)}npS;  
    end abtAkf  
end ;KmSz 1A  
% 计算P(ui|h1) JLRw`V,o7  
for i = 1:2 k@AOE0m  
    for j = 1:2 Bya!pzbpr  
        P_u1_h1(i,j) = P(2,i,2)*P(3,j,2); fw_V'l#\  
    end L&lNpMT  
end gQ*0Mk  
for i = 1:2 K.] *:fd  
    for j = 1:2 (?&X<=|"  
        P_u2_h1(i,j) = P(1,i,2)*P(3,j,2); q]tPsX5{*  
    end jj#K[@u  
end .8CR \-
  
for i = 1:2 dKw*L|5  
    for j = 1:2 Tc@r#!.m  
        P_u3_h1(i,j) = P(1,i,2)*P(2,j,2); '1=t{Rw
 
    end 0?ZJJdI3  
end &?*M+q34  
%%%计算新门限值T %%% -ny[Lh^b  
numerator = zeros(1,3); q[l},nw  
denominator = zeros(1,3); ]]+wDhxH  
for i = 1:2 KLs%{'[7:  
    for j = 1:2 u9e A"\s  
        %% 计算第1个传感器新门限值分子与分母的求和部分 w1aa5-aF  
        numerator(1) = numerator(1) + A_u1(i,j)*P_u1_h0(i,j); H;}V`}c
<`  
        denominator(1) = denominator(1) + A_u1(i,j)*P_u1_h1(i,j); y1/$dn  
    end CJ&0<Z}{m  
end Fp-d69Npo  
for i = 1:2 bxO8q57  
    for j = 1:2 `w]=xe  
        %% 计算第2个传感器新门限值分子与分母的求和部分 rZ1${/6  
        numerator(2) = numerator(2) + A_u2(i,j)*P_u2_h0(i,j); VVvV]rU~  
        denominator(2) = denominator(2) + A_u2(i,j)*P_u2_h1(i,j); vI#\Qe  
    end ]xA;*b;|h  
end uA:|#mO  
for i = 1:2 xF5q=%n  
    for j = 1:2 \no[>L]  
         numerator(3) = numerator(3) + A_u3(i,j)*P_u3_h0(i,j); K3TMTY<p  
        denominator(3) = denominator(3) + A_u3(i,j)*P_u3_h1(i,j); ;]MHU/  
    end qr(SAIX"  
end m"~$JA u  
if(denominator(1) == 0) ooByGQ90V:  
     break; cxrUk$f  
end U=p,drF,A  
if(denominator(2) == 0) 5FnWlFc  
     break; a>Uk<#>2?a  
end 4W~pAruwr  
if(denominator(3) == 0) ) ]U-7  
     break; J \1&3r|R  
end Zo22se0)  
for i = 1:3 I* \o  
    T(i) = C_F*numerator(i) / (C_D*denominator(i)); #Z}\;a{vZ  
end wCvtw[6  
if(abs(RB-old_RB) < epsilon) (D{Fln\  
    break; |~CN]N  
else qp_k
ILo~  
    old_RB = RB; :sFP{rFx~  
end mTW0_!.  
if(whileindex == 20) (H<S&5[  
    break; yEy} PCJ&  
end Nj qUUkc  
-d6|D?}S  
end g%#" 5Kr  
result(index+1) = RB; R|ViLty  
end .ev'd&l.  
%               第三步   画贝叶斯风险值随先验概率P0变化曲线                 ^$24231^  
x = 0:0.05:1; FlRbGg^  
plot(x,result,'r'); ]6p?mBuQ  
title('贝叶斯风险值-先验概率P0曲线'); kp[+Iun?  
xlabel('先验概率P0'); I2qC,Nkk  
ylabel('贝叶斯风险值'); ykxjT@[