xa=0.23,xb=0.82，w1=0.1,w2=0.5,w3=0.4,w4=0.3，zc=xaw1+xbw2，yc=Sigmoid(zc)， yd=Sigmoid(xaw2+xbw4)，w5=0.2,w6=0.6，yf=Sigmoid(ycw5+ydw6)，写matlab代码，Compute mean square error E =(1 − yf)^2/2, 1 is the desired output

Sure, here's the Matlab code to compute the mean square error E for the given values:

xa = 0.23;
xb = 0.82;
w1 = 0.1;
w2 = 0.5;
w3 = 0.4;
w4 = 0.3;
w5 = 0.2;
w6 = 0.6;

% Calculate yc and yd
zc = xa*w1 + xb*w2;
yc = 1 / (1 + exp(-zc));
yd = 1 / (1 + exp(-xa*w3 - xb*w4));

% Calculate yf
yf = 1 / (1 + exp(-yc*w5 - yd*w6));

% Calculate the mean square error E
E = (1 - yf)^2 / 2;

In this code, we first define the given values for xa, xb, and the weights w1-w6. Then, we calculate the intermediate values of zc, yc, and yd using the sigmoid function. Finally, we calculate the predicted output yf using the intermediate values and the weights w5 and w6, and then compute the mean square error E using the desired output value of 1 and the value of yf.