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

Qinge Wu,Zhiwu Chen,Zhenyu Han,Dong Sun,Xing Wang,Hu Chen

doi:10.5755/j01.itc.50.1.25412

Qinge Wu, Zhiwu Chen + Show 4 more

Open Access

https://doi.org/10.5755/j01.itc.50.1.25412

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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%.

Highlights

In the aircraft fault monitoring and diagnosis, when the aircraft fails due to the different structure of the aircraft components, the fault frequency of the different components included in the vibration signal is distributed in different frequency ranges
Sharma et al [17] studied the concepts of homomorphism, fuzzy multiset transformation semigroup and coverings of fuzzy multiset finite automata, and established their basic properties for the algebraic study of fuzzy multiset automata theory
Works had been shown how fuzzy finite-state automata (FFA) can be mapped into recurrent neural networks with second-order weights using a crisp representation of FFA states [14]

Summary

Introduction

In the aircraft fault monitoring and diagnosis, when the aircraft fails due to the different structure of the aircraft components, the fault frequency of the different components included in the vibration signal is distributed in different frequency ranges. If the aircraft hides an early weak defect of a component, its defect information is overwhelmed by the running vibration signals and random noise of other components. Blanco et al [3] discussed fuzzy knowledge equivalence representations between neural networks, fuzzy systems and models of automata. From a control point of view, fuzzy finite-state automata (FFA) with recurrent neural networks [11] for often imitating fuzzy dynamical systems were very useful. Works had been shown how FFA can be mapped into recurrent neural networks with second-order weights using a crisp representation of FFA states [14]

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