Perceptron learning rule past exam question
Firstly let me say that I am not going to solve this for you. What I would do instead is help you figure out how to solve it all by yourself.


How many inputs will your perceptron have if your input vector is in n-D?
Conceptually, what does the output from a perceptron represent?
Following from the second point, what does updating the weights do to the output from your perceptron?
Ideally, what should the updating of the weights do to the output?
When do you need to update the weights?


Think about this by plotting the inputs and output from the perceptron on a graph.

These are some of the basic concepts you need to understand to get the basics of Artificial Neural Networks (ANNs).

Good Luck