The improved grey model by fusing exponential buffer operator and its application

Zhou, Wei; Ding, Bingqing; Zhang, Ying; Bush, Anthony

doi:10.3233/jifs-17419

Cited by 4 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After combining the GM(1,1) model with the exponential buffer operator, a relatively high computational accuracy was achieved because of the advantages provided by fitting in a small-sample environment and by the buffer operator in dealing with interference factors. 12 However, because of the lack of information and the large uncertainties of small samples, the small-sample theory is still inadequate. Thus, developing and improving the small-sample theory for the service life prediction and reliability evaluation of mechanical parts and components have become key factors in the R&D of products with long life and high reliability.…”

Section: Introductionmentioning

confidence: 99%

Life prediction of heavy-load self-lubricating liners

Hao

Wang

Pan

2021

Advances in Mechanical Engineering

View full text Add to dashboard Cite

To address the issues of long testing periods and small sample sizes while evaluating the service life of heavy-load self-lubricating liners, we propose a succinct method based on Monte Carlo simulation that is significantly fast and requires a small sample size. First, the support vector regression algorithm was applied to fit the degradation trajectories of the wear depth, and the first and second characteristic parameter vectors of the wear depth as well as the corresponding distribution models were obtained. Next, sample expansion was performed using Monte Carlo simulation and the inverse transform method. Finally, based on the failure criterion of the self-lubricating liner, the service lives and distribution models of the expanded samples were obtained; subsequently, the corresponding reliability life indices were provided. Our results indicate that when the expanded sample was large enough, the proposed prediction method exhibited a relatively high prediction accuracy. Therefore, these results provide theoretical support for shortening the testing cycle used to evaluate the service life of self-lubricating liners and for accelerating the research and development of self-lubricating spherical plain bearing products.

show abstract

Section: Introductionmentioning

confidence: 99%

Life prediction of heavy-load self-lubricating liners

Hao

Wang

Pan

2021

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Non-ferrous metal futures market is also produced in this context [17].China is an important non-ferrous metal trading nation, and some large import and export companies took part in futures trading in the international market as early as the beginning of 80s.They invited futures experts from abroad to teach futures theory and futures trading practice, which led to the establishment of non-ferrous metal futures market by the relevant departments. After the Shenzhen nonferrous metals exchange was set up for half a year, the former department and Shanghai Municipal People's Government jointly submitted to the State Council "request for instructions on trial in Shanghai Nonferrous Metals Exchange", Stating the necessity, the pilot exchange basic conditions, basic ideas and construction progress [18].…”

Section: The Emergence Of Nonferrous Metal Futuresmentioning

confidence: 99%

Study on the development of nonferrous metal futures market

Hu¹,

Sun²,

Zhou³

2017

Proceedings of the 2nd International Symposium on Business Corporation and Development in South-East and South Asia Under B$R I

Self Cite

View full text Add to dashboard Cite

Abstract-Since the introduction of the first commodity futures standard contract -Super aluminum futures contract in 1991, the metal futures market has developed for nearly twenty-five years.Although China's metal futures market overall development time is not long, but the development rate of the world attention, has a higher degree of market standardization, in the international metal futures market, the role and impact of increasingly prominent.The price of nonferrous metals futures has become an important benchmark price of the nonferrous metals industry in China, and has a great influence on the international market.

show abstract

“…By fusing exponential buffer operator and its application, Zhou etc. improved grey model and made the model become more accurate [18].…”

Section: Introductionmentioning

confidence: 99%

The predicted value of Environment pollution treatment project investment based on the GM(1,1) model

Kou¹,

Sun²,

Zhou³

2017

Proceedings of the 2nd International Symposium on Business Corporation and Development in South-East and South Asia Under B$R I

Self Cite

View full text Add to dashboard Cite

Abstract-The predicted value of Environment pollution treatment project investment is very significant in our country. It has less samples and uncertain information, a small dynamic sample prediction which is in view of the environmental pollution treatment project investment is very important .The GM(1,1) model is very appropriate. Combining with the classical GM(1,1) model through a process of accumulation of data, generating the immediate value sequence, constructing the grey whitening equation and selecting this project investment in our country over the 6 years as samples, fitting and forecasting the project and confirming the feasibility, practicality and veracity of the environmental pollution treatment project investment by combining with classic GM (1, 1) model has a certain guiding significance.

show abstract

The improved grey model by fusing exponential buffer operator and its application

Cited by 4 publications

References 25 publications

Life prediction of heavy-load self-lubricating liners

Life prediction of heavy-load self-lubricating liners

Study on the development of nonferrous metal futures market

The predicted value of Environment pollution treatment project investment based on the GM(1,1) model

Contact Info

Product

Resources

About