A multi-variable grey model with a self-memory component and its application on engineering prediction
- Submitting institution
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De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11136
- Type
- D - Journal article
- DOI
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10.1016/j.engappai.2015.03.014
- Title of journal
- Engineering Applications of Artificial Intelligence
- Article number
- -
- First page
- 82
- Volume
- 42
- Issue
- -
- ISSN
- 0952-1976
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
-
-
- Citation count
- 36
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel multi-variable grey self-memory coupling prediction model (SMGM(1,m)) significantly improves grey prediction models and led to a number of new research development, such as the grey Bernoulli self-memory model, B-spline multivariate grey model, grey self-memory combined model, non homogeneous multivariable grey model and convolution integral based multivariable grey model. It has been applied in condition monitoring, emission control, gas pipeline evaluation, Sino-Russian trade evaluation, solar energy generation forecasting and complex equipment cost estimation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -