Grey Self-memory Combined Model for Complex Equipment Cost Estimation

Date

2017-01

Advisors

Journal Title

Journal ISSN

ISSN

0957-3720

DOI

Volume Title

Publisher

Research Information Ltd.

Type

Article

Peer reviewed

Yes

Abstract

To improve the using rationality of complex equipment cost, this paper presents a novel grey self-memory combined model for predicting the equipment cost. The proposed model can improve the modeling accuracy by means of the self-memory prediction technique. The combined model combines the advantages of the self-memory principle and traditional grey 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 complex equipment cost estimation, the novel grey self-memory combined 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 robustness of the combined model, and its superior predictive performance over other traditional grey prediction models. The results show that the proposed combined model enriches equipment cost estimation methods, and can be applied to other similar complex equipment cost estimation problems.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

complex equipment cost estimation, grey system model, self-memory prediction technique, combined prediction mode

Citation

Guo, X., Liu, S., Yang, Y., Wu, L. (2017) Grey Self-memory Combined Model for Complex Equipment Cost Estimation. The Journal of Grey System, 29(1), pp. 78-92.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)