SOLVE part b only.. please write a correct matlab code

Given a three$-$layer perceptron with two inputs $(x_1$ and $x_2),$ a hidden layer containing two neurons, and an output neuron, you are provided with the initial synaptic weights as follows: $w_{31}=0\mathrm{.}62,w_{32}=0\mathrm{.}55,w_{41}=0\mathrm{.}42,w_{42}=-0\mathrm{.}17,w_{53}=0\mathrm{.}35,$ and $w_{54}=0\mathrm{.}81.$ The learning rate $()$ is set to $0\mathrm{.}25.$ Given that the first input vector is $0,1$ with a desired output of $0,$ and the second input vector is $1,1$ with a desired output of $1,$

a$.$ calculate the new synaptic weights after completing two iterations of the backpropagation training process. Assume the sigmoid activation function for neurons.

b$.$ create a code that generates a table listing all parameters involved in the computation for both iterations. These parameters include the input to neurons $(v_3,v_4,v_5),$ output of neurons $(y_3,y_4,y_5),$ error at the output neuron $(e_5),$ error signals for gradient descent $({}_5,{}_3,{}_4),$ and all six weights $(w_{31},w_{32},w_{41},w_{42},w_{53},w_{54}).$

$W_{21}$