I have a multilayer perceptron. It has an input layer with two neurons, a hidden layer with an arbitrary number of neurons, and an output layer with two neurons.

Given that `randomboolean` and `targetboolean` are random boolean values, and the network operates as such:

```
input(randomboolean); //Set the input neurons to reflect the random boolean
propagateforwards(); //Perform standard forward propagation
outputboolean = output(); //To get the networks output
ideal(targetboolean); //Performs connection updating via back-prop

```

Is it possible to get the network to map the `randomboolean` value to the `targetboolean` value in such a way as the the `outputboolean` value will correctly match the `targetboolean` while running in an 'on-line' (where prediction occurs along with continued learning) mode after some arbitrary number of training cycles. 

I hear that the network needs to be recurrent to process this as it may be temporal behaviour, however the MLP is a universal computing platform and I assume it should be able to approximate the temporal behaviour needed for this task.