Inducing finite state machines from training samples using ant colony optimization
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A method for control finite state machine (FSM) induction in which an ant colony optimization algorithm is used for search optimization is proposed. The efficiency of this method is estimated using the generation of FSMs for controlling a model of an unmanned aerial vehicle (UAV). It is shown that the proposed method outperforms the method based on genetic algorithms both in terms of performance and quality.
KeywordsTraining Sample Unmanned Aerial Vehicle System Science International Finite State Machine Continuous Output
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