A perceptron with a unipolar activation function has two inputs with weights $w_1=0\mathrm{.}2$ and

$w_2=-0\mathrm{.}5,$ and a bias $b=-0\mathrm{.}2(b$ can therefore be considered as a weight for an extra input

which is always set to $-1).$ The perceptron is trained using the learning rule

$w=(d-y)x$

where $x$ is the input vector, $$ is the learning rate, $w$ is the weight vector, $d$ is the desired

output, and $y$ is the actual output.

What are the new values of the weights and threshold after one step of training with the input

vector $x=([1,1]{)}^T$ and desired output $d=1,$ using a learning rate $=0\mathrm{.}2?$