A multi-variable grey model with a self-memory component and its application on engineering prediction. (June 2015)

• Record Type: Journal Article
• Authors: Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Gao, Yanbo; Yang, Yingjie
• Is Part Of: Engineering applications of artificial intelligence. Volume 42(2015:Jun.)
• Page Start: 82
• Page End: 93
• Publication Date: 2015-06
• Subjects: Grey prediction theory -- Multi-variable system -- MGM(1, m) model -- Self-memory principle -- Subgrade settlement -- Foundation pit deformation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.03.014
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents a novel multi-variable grey self-memory coupling prediction model (SMGM(1, m)) for use in multi-variable systems with interactional relationship under the condition of small sample size. The proposed model can uniformly describe the relationships among system variables and improve the modeling accuracy. The SMGM(1, m) model combines the advantages of the self-memory principle of dynamic system and traditional MGM(1, m) model through coupling of the above two prediction methods. The weakness of the traditional grey prediction model, i.e., being sensitive to initial value, can be overcome by using multi-time-point initial field instead of only single-time-point initial field in the system׳s self-memorization equation. As shown in the two case studies of engineering settlement deformation prediction, the novel SMGM(1, m) model can take full advantage of the system׳s multi-time historical monitoring data and accurately predict the system׳s evolutionary trend. Three popular accuracy test criteria are adopted to test and verify the reliability and stability of the SMGM(1, m) model, and its superior predictive performance over other traditional grey prediction models. The results show that the proposed SMGM(1, m) model enriches grey prediction theory, and can be applied to other similar multi-variable engineering systems. Highlights: The self-memory principle is introduced into the grey MGM(1, m) prediction model. We can uniformly describe the interactional relationship of multi-variable systems. The coupling prediction model can take full advantage of its multi-time historical data. Traditional grey model׳s weakness of being sensitive to initial value can be overcome. The results of engineering example demonstrate its remarkable prediction performance.