Browsing by Author "Guo, Xiaojun"

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    A generalized grey model with symbolic regression algorithm and its application in predicting aircraft remaining useful life
    (Elsevier, 2024-07-18) Liu, Lianyi; Liu, Sifeng; Yang, Yingjie; Guo, Xiaojun; Sun, Jinghe
    As a sparse data analysis method, a grey model faces challenges in interpretability for its effective application in uncertain systems. This study proposes a generalized grey model (GGM) based on symbolic regression, designed to improve the intelligence and adaptability of grey models. The GGM serves as a unified framework, integrating various grey model families and addresses regression challenges to determine the model structure. Symbolic regression in the GGM identifies symbolic input-output relationships, offering an interpretable approach for structure determination. By leveraging the non-uniqueness principle in grey system theory and employing structural penalty parameters, the model balances complexity and interpretability. A comparative analysis between GGM and conventional grey function models is conducted focusing on the differences in modeling, structure identification, and parameter optimization. Validation on the M3 competition dataset demonstrated the GGM's superior performance, achieving a significant reduction in prediction error compared to other grey forecasting models. Additionally, a rigorous analysis of aircraft lifespan data underscored the robustness and accuracy of GGM in practical engineering applications.
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    An Optimized Damping Grey Population Prediction Model and Its Application on China’s Population Structure Analysis
    (MDPI, 2022-10-18) Guo, Xiaojun; Zhang, Rui; Shen, Houxue; Yang, Yingjie
    Population, resources and environment constitute an interacting and interdependent whole. Only by scientifically forecasting and accurately grasping future population trends can we use limited resources to promote the sustainable development of society. Because the population system is affected by many complex factors and the structural relations among these factors are complex, it can be regarded as a typical dynamic grey system. This paper introduces the damping accumulated operator to construct the grey population prediction model based on the nonlinear grey Bernoulli model in order to describe the evolution law of the population system more accurately. The new operator can give full play to the principle of new information first and further enhance the ability of the model to capture the dynamic changes of the original data. A whale optimization algorithm was used to optimize the model parameters and build a smooth prediction curve. Through three practical cases related to the size and structure of the Chinese population, the comparison with other grey prediction models shows that the fitting and prediction accuracy of the damping accumulated–nonlinear grey Bernoulli model is higher than that of the traditional grey prediction model. At the same time, the damping accumulated operator can weaken the randomness of the original data sequence, reduce the influence of external interference factors, and enhance the robustness of the model. This paper proves that the new method is simple and effective for population prediction, which can not only grasp the future population change trend more accurately but also further expand the application range of the grey prediction model.
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    Predicting the trend of infectious diseases using grey self-memory system model: a case study of the incidence of tuberculosis
    (Elsevier, 2021-11-22) Guo, Xiaojun; Shen, Houxue; Liu, Sifeng; Xie, Naiming; Yang, Yingjie; Jin, Jingliang
    Objectives The prediction and early warning of infectious diseases is an important work in the field of public health. This study constructed the grey self-memory system model to predict the incidence trend of infectious diseases affected by many uncertain factors. Study design The design of this study is a combination of the prediction method and empirical analysis. Methods By organically coupling the self-memory algorithm with the mean GM(1,1) model, the tuberculosis incidence statistics of China from 2004 to 2018 were selected for prediction analysis. Meanwhile, by comparing with the other traditional prediction methods, three representative accuracy check indexes (MSE, AME, MAPE) were conducting for error analysis. Results Owing to the multiple time-points initial fields, which replace the single time-points, the limitation of the traditional grey prediction model, which is sensitive to the initial value, is overcome in the self–memory equation. Consequently, compared with the mean GM model and other statistical methods, the grey self-memory model shows significant forecasting advantages, and its single-step rolling prediction accuracy is superior to other prediction methods. Therefore, the incidence of tuberculosis in China in the next year can be predicted as 55.30 (unit: 1/105). Conclusions The grey self-memory system model can closely capture the individual random fluctuation in the whole evolution trend of the uncertain system. It is appropriate for predicting the future incidence trend of infectious diseases and is worth popularizing to other similar public health prediction problems.
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    Research on Index System for Disabled Elders Evaluation and Grey Clustering Model Based on End-point Mixed Possibility Functions
    (Research Information Ltd, 2019-11-01) Liu, Sifeng; Fan, Yun; Yang, Yingjie; Tan, Xuerui; Fang, Zhigeng; Chen, Yequn; Zhang, Qin; Xie, Naiming; Yuan, Chaoqing; Xue, Qingyuan; Tao, Liangyan; Guo, Xiaojun
    An operational ability assessment system for older adults is of great help to address health and social challenges for ageing. In this paper, the main problems in currently available ADL and ability evaluation systems have been analyzed. The basic principles to build an index system for disability elders evaluation have been put forwarded. Then，an improved Barthel index system for ADL evaluation and a new older adults ability evaluation system consisted of 4 first-level indexes and 14 secondary indexes based on experts’ opinion and the ability assessment system for older adults by Ministry of Civil Affairs of China have been built. The grey clustering model based on end-point mixed triangular possibility function has been introduced. And three living examples of adults’ disability evaluation have been conducted. It is confirmed clearly that the three older adults belong to different categories of "severe disability", "mild disability", and "ability passable" respectively. The research results can be used as reference for government to formulate the elderly-care policies, to run and allocate the elderly-care resources, as well as reference for various nursing or elderly-care institutions.