This network is big enough for the XOR problem and I can't see any obvious mistakes, so I suspect it's getting stuck in a local minimum.
Try going through the training set 10,000 times instead of 1000; this gives it a better chance of breaking out of any minima and converging. You can also increase convergence a lot by upping the number of hidden neurons, tweaking η (the learning rate) or adding momentum. To implement the latter, try using this as your training function:
this.train = function(learningRate) {
var momentum = 0 /* Some value, probably fairly small. */;
self.neurons.forEach(function(neuron) {
neuron.bias.weight += neuron.bias.delta * learningRate;
neuron.bias.delta = 0;
neuron.input.forEach(function(input) {
input.factor.weight += (input.factor.delta * learningRate) + (input.factor.weight * momentum);
input.factor.delta = 0;
})
})
}
I've had good results changing the learning rate to 1.5 (which is pretty high) and momentum to 0.000001 (which is pretty small).
(Incidentally, have you tried running the .NET implementation with a few different seeds? It can take quite a while to converge too!)