  We develop a learning algorithm for closed signal flow graphs - a graphical
model of signal transducers. The algorithm relies on the correspondence between
closed signal flow graphs and weighted finite automata on a singleton alphabet.
We demonstrate that this procedure results in a genuine reduction of
complexity: our algorithm fares better than existing learning algorithms for
weighted automata restricted to the case of a singleton alphabet.
