Algorithms for making neural nets converge

I am currently using a simple PID, and I was wondering if there's a faster one.
I know about backpropagation, genetic/evolutionary algorithms and steepest/conjugate gradient descent, but they all seem to me just fancy ways of doing PID and I can never get them to work better than PID, which really demotivates implementing them any further.
Still, I hate how slow the convergence is, so I was wondering if there's some sort of magic algorithm someone here knows about. (linear/superlinear convergence? I wish)

What are you working on? I've always had more success with support vector machines and a simple radial basis kernel than with neutral networks. Unless you're working on some sophisticated artificial intelligence with a team of engineers and a server farm, there are much more practical learning algorithms at your disposal.