Abstraction of circuits is desirable for faster simulation and high-level system verification. In this paper, we present an algorithm that derives a Mealy machine from differential equations of a circuit by learning input-output trajectories. The key idea is adapted from Angluin's DFA (deterministic finite automata) learning algorithm that learns a DFA from another DFA. Several key components of Angluin's algorithm are modified so that it fits in our problem setting, and the modified algorithm also provides a reasonable partitioning of the continuous state space as a by-product. We validate our algorithm on a latch circuit and an integrator circuit, and demonstrate that the resulting FSMs inherit important behaviors of original circuits.
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