How to train a single Artificial Neuron to predict f(x), where f(x)=x Conditions : 1. Network architecture: one single neuron.
2. x=99 (real number)
3. input to the neuron is x, output must be f(x) after trained.
4. use one bias and one weight.
5. use MSE for calculating error.
6. use gradient descent(original version) for back propagation.
7. learning rate: 0.01, no decay, no momentum.
8. must assign random value of weight and bias  
will it work for this problem?
This will not work.
because, for this problem x=99, and when finding gradients, the results will be large numbers and updating weight and bias using learning rate 0.01 will take huge steps, and so the bias and weight will cross the solutions.
using learning rate less than 0.001 will solve this problem.
I tried learning rate 0.0001 and it worked.