Application of Fuzzy Pushdown Automaton on Prediction of Quality Control for Spinning Yarn

Abstract

In order to perform better recognition, tracking and control for fuzzy and uncertain thing, this paper will design a suitable fuzzy pushdown automaton (FPDA) control method to solve the problem. Firstly, the control design structure of FPDA and the decision reasoning rules in control are given. Secondly, the application of FPDA in prediction of quality control for spinning yarn is discussed in the practical problem. Finally, the comparison of FPDA and other control methods on the target control is given. The simulation results show that the control speed and the average precision of designed FPDA are faster by12ms and higher by 4.98% than that of traditional method, which its control precision is 96.87%.

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“…Fuzzy automata are used to handle system uncertainties more accurately where classical automata fail to cater to the circumstances. Fuzzy automata have been frequently employed since the introduction of fuzzy technology and neural networks [7][8][9][10][11][12][13]. Furthermore, there were a variety of problems to be resolved, for example, a car anticrash radar, freeway management, urban road traffic control, and obstacle recognition in front of a vehicle which required flexible, quick, and accurate decisions.…”

Fault Diagnosis in Regenerative Braking System of Hybrid Electric Vehicles by Using Semigroup of Finite-State Deterministic Fully Intuitionistic Fuzzy Automata

“…Fuzzy automata are used to handle system uncertainties more accurately, whereas classical automata fail to cater to the circumstances. Fuzzy automata have been frequently employed since the introduction of fuzzy technology and neural networks [3][4][5][6][7][8][9][10][11][12][13]. Furthermore, there were a variety of problems to be resolved, for example, medical diagnosis, car anti-crash radar, freeway management, urban road traffic control, and obstacle recognition in front of a vehicle, which required flexible, quick, and accurate decisions, and then, fuzzy neural network automata (FNNA) [14][15][16][17] are an excellent choice.…”