Learning Closed Signal Flow Graphs
This work addresses a domain-specific problem in computational models for signal processing, offering an incremental improvement over existing methods.
The paper tackles the problem of learning closed signal flow graphs, a graphical model for signal transducers, by developing an algorithm based on their correspondence with weighted finite automata on a singleton alphabet. The result is a reduction in complexity, with the algorithm outperforming existing learning algorithms for weighted automata in this specific case.
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.