Identification of unknown Petri net structures from growing observation sequences
This thesis proposed an algorithm that can find optimized Petri nets from given observation sequences according to some rules of optimization. The basic idea of this algorithm is that although the length of the observation sequences can keep growing, we can think of the growing as periodic and algorithm deals with fixed observations at different time. And the algorithm developed has polynomial complexity. An segment of example code programed according to this algorithm has also been shown. Furthermore, we modify this algorithm and it can check whether a Petri net could fit the observation sequences after several steps. The modified algorithm could work in constant time. These algorithms could be used in optimization of the control systems and communication networks to simplify their structures.
Li, Purdue University.
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