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.

翻译：我们提出了一种针对闭信号流图——信号转换器的一种图形模型——的学习算法。该算法基于闭信号流图与单字母表上加权有限自动机之间的对应关系。我们证明此过程能真正降低复杂度：在单字母表的限定情况下，我们的算法表现优于现有的加权自动机学习算法。