Learning Closed Signal Flow Graphs

TL;DR

This paper introduces a novel learning algorithm for closed signal flow graphs, leveraging their relationship with weighted finite automata, resulting in improved efficiency over existing methods for singleton alphabets.

Contribution

The paper presents a new learning algorithm for closed signal flow graphs that outperforms existing weighted automata learning algorithms in the singleton alphabet case.

Findings

Algorithm reduces complexity compared to previous methods.

Demonstrates better performance for singleton alphabet weighted automata.

Establishes a correspondence between signal flow graphs and finite automata.

Abstract

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.