Grammatical interface for even linear languages based on control sets
Information Processing Letters
Polynomial Time Learnability of Simple Deterministic Languages
Machine Learning
Inference of Sequential Machines from Sample Computations
IEEE Transactions on Computers
Sequential Machine Identification
IEEE Transactions on Computers
On the Synthesis of Finite-State Machines from Samples of Their Behavior
IEEE Transactions on Computers
Heavy-traffic analysis of cloud provisioning
Proceedings of the 24th International Teletraffic Congress
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This paper presents a method for the identification of concurrent Discrete Event Systems (DES) from input-output sequences representing the observed behavior. The proposed off-line technique yields an input-output model expressed as an Interpreted Petri net (IPN), which represents exactly the language than that generated by the observed system, which may include cyclic sequences. First, a sample of input-output vectors words are processed for obtaining sequences of output changes called events; then sequences of κ-length event traces are built and represented by an IPN model composed by non measurable places. Then measurable places are added; they are related to transitions representing pertinent output changes. Finally inputs are associated to transitions and implicit non measurable places are removed.