Application of the fuzzy logic theory in the problem prediction values of the technical coefficient

Korobkin, E. A.; Kurklinskaya, E. Yu; Astachova, Irina; Shashkin, A. I.

doi:10.1088/1742-6596/1202/1/012010

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…Fuzzy recognition consists of three steps: extracting features, establishing membership functions, and establishing recognition criteria. 24 We define the value range of the data as U and conduct fuzzy state divisions s 1 , s 2 , . . . , s n . If ∀ u ∈ U …”

Section: Methodsmentioning

confidence: 99%

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Prediction and analysis of bearing vibration signal with a novel gray combination model

Yuan

Sun

Zhou

et al. 2014

Advances in Mechanical Engineering

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Bearings are the core components of ship propulsion shafting, and effective prediction of their working condition is crucial for reliable operation of the shaft system. Shafting vibration signals can accurately represent the running condition of bearings. Therefore, in this article, we propose a new model that can reliably predict the vibration signal of bearings. The proposed method is a combination of a fuzzy-modified Markov model with gray error based on particle swarm optimization (PGFM (1,1)). First, particle swarm optimization was used to optimize and analyze the three related parameters in the gray model (GM (1,1)) that affect the data fitting accuracy, to improve the data fitting ability of GM (1,1) and form a GM (1,1) based on particle swarm optimization, which is called PGM (1,1). Second, considering that the influence of historical relative errors generated by data fitting on subsequent data prediction cannot be expressed quantitatively, the fuzzy mathematical theory was introduced to make fuzzy corrections to the historical errors. Finally, a Markov model is combined to predict the next development state of bearing vibration signals and form the PGFM (1,1). In this study, the traditional predictions of GM (1,1), PGM (1,1), and newly proposed PGFM (1,1) are carried out on the same set of bearing vibration data, to make up for the defects of the original model layer by layer and form a set of perfect forecast system models. The results show that the predictions of PGM (1,1) and PGFM (1,1) are more accurate and reliable than the original GM (1,1). Hence, they can be helpful in the design of practical engineering equipment.

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Section: Methodsmentioning

confidence: 99%

“…PGM (1,1) analysis example. The values of the optimized gray prediction models are predicted using equations (24) and (25). The fitting data results are shown in Table 3, and the comparison of the effect of the errors is shown in Figure 4 Table 1.…”

Section: Pgm (11) Calculation and Analysismentioning

confidence: 99%

Prediction and analysis of bearing vibration signal with a novel gray combination model

Yuan

Sun

Zhou

et al. 2014

Advances in Mechanical Engineering

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“…For the application of fuzzy theory based on information security in the prediction of athletes' economic status, a large number of researchers at home and abroad have obtained their own research results. Korobkin et al used the experimental method and case study method in their study on fuzzy theory, compared the state of athletes after physical training with that before physical training, and concluded that the use of fuzzy theory can get better prediction results by eliminating interference factors under di erent physical conditions of athletes [1]. Sun used the investigation method and experimental research method in its research to exclude other interference factors among different athletes.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Theory in the Prediction of Athletes' Competitive State Based on Information Security

Song

Zhong

2022

Mobile Information Systems

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In today’s highly competitive market, a large number of high-tech products have been used by the athletesin various academic competitions. At present, a large number of athletes have used their own rigorous theories to predict the effects of modern games. In this study, the fuzzy theory calculation method based on information security is used to install related detection equipment on athletes. Through long-term data collection, the data is input into the computing system. After analysis, we can get the athlete’s body cycle, 6 days is a short cycle, 28 days is a medium cycle, and 128 days is a long cycle. Through the adjustment of training items and training time, the athlete’s competitive state can be adjusted to achieve good results in the competition. This article also tested and experimentally studied the differences in the emotions and mental states of the athletes in coping with the competition, and tested the mental states of the two groups of athletes before the start of the competition. The results showed that the data for active response, balanced response, and negative response were 36%, 32%, and 32%, respectively. Under the coach’s guidance and persuasion, the data on athletes’ coping styles from negative to positive has been significantly improved, reaching 67%. The experiments of this research prove that through the use of fuzzy theory based on information security and related high-tech equipment, it is possible to effectively predict and adjust the athlete’s competitive state. Before the arrival of important events, adjust the training intensity from the 6-day and 28-day cycles to make the athletes’ competitive state reach the expected excellent condition.

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Application of the fuzzy logic theory in the problem prediction values of the technical coefficient

Cited by 2 publications

References 2 publications

Prediction and analysis of bearing vibration signal with a novel gray combination model

Prediction and analysis of bearing vibration signal with a novel gray combination model

Fuzzy Theory in the Prediction of Athletes' Competitive State Based on Information Security

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