Abstract
In order to perform better in target control, this paper proposed a decision-making system method based on fuzzy automata. The decision-making system first preprocessed the signal and then performed a two-level decision on the target to achieve optimal control. The system consisted of four parts: signal preprocessing, contrast decision-making, comprehensive judgment of decision-making and decision-making result. These decision algorithms in target control were given. A concrete application of this decision-making system in target control was described. Being compared with other existing methods, this paper used both global features and local features of target, and used the decision-making system of fuzzy automata for the target control. Simulation results showed that the control effect based on the decision-making system was better than that of the other existing methods. Not only it was faster, but also its correct control rate was higher to be 95.18% for the target control. This research on the control system not only developed the FA theory, but also strengthened its application scope in the field of control engineering.
Highlights
At present, there are few researches on fuzzy signal processing based on inference system of fuzzy automata (FA) and fuzzy image comprehension in practical engineering application field
In order to speed up to solve this problem, this paper studied the target control method based on fuzzy automata, because FA can more objectively process various ambiguous cases and complicated things
This paper focuses on the establishment of target control system of fuzzy automata (FA)
Summary
There are few researches on fuzzy signal processing based on inference system of fuzzy automata (FA) and fuzzy image comprehension in practical engineering application field. In order to speed up to solve this problem, this paper studied the target control method based on fuzzy automata, because FA can more objectively process various ambiguous cases and complicated things. The experimental results showed that a second-order feedback neural network using a real-time, forward training algorithm could derive the formal grammar from the positive and negative string training samples by learning. They used a heuristic method during and after neural network which was trained to extract an automaton. The simulation results show that its correct control rate is as high as 95.18%
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