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COMPUTER PROGRAMMING (25 journals)

Showing 1 - 25 of 25 Journals sorted alphabetically
ACM SIGPLAN Fortran Forum     Full-text available via subscription   (Followers: 3)
ACM Transactions on Programming Languages and Systems (TOPLAS)     Hybrid Journal   (Followers: 18)
Acta Informatica     Hybrid Journal   (Followers: 5)
Advances in Image and Video Processing     Open Access   (Followers: 28)
Algorithmica     Hybrid Journal   (Followers: 9)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 12)
Computer Methods and Programs in Biomedicine     Hybrid Journal   (Followers: 6)
Constraints     Hybrid Journal  
Grey Systems : Theory and Application     Hybrid Journal  
International Journal of Parallel Programming     Hybrid Journal   (Followers: 6)
International Journal of People-Oriented Programming     Full-text available via subscription  
International Journal of Soft Computing and Software Engineering     Open Access   (Followers: 14)
Journal of Computer Languages     Hybrid Journal   (Followers: 5)
Journal of Functional Programming     Hybrid Journal   (Followers: 1)
Journal of Logical and Algebraic Methods in Programming     Hybrid Journal   (Followers: 1)
Linux Journal     Full-text available via subscription   (Followers: 25)
Mathematical and Computational Applications     Open Access   (Followers: 3)
Mathematical Programming     Hybrid Journal   (Followers: 15)
Optimization: A Journal of Mathematical Programming and Operations Research     Hybrid Journal   (Followers: 6)
Proceedings of the ACM on Programming Languages     Open Access   (Followers: 5)
Programming and Computer Software     Hybrid Journal   (Followers: 16)
Python Papers     Open Access   (Followers: 11)
Python Papers Monograph     Open Access   (Followers: 4)
Science of Computer Programming     Hybrid Journal   (Followers: 14)
Theory and Practice of Logic Programming     Hybrid Journal   (Followers: 3)
Similar Journals
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Grey Systems : Theory and Application
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2043-9377 - ISSN (Online) 2043-9385
Published by Emerald Homepage  [362 journals]
  • On some operations on grey graphs with application

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      Authors: Mohammed Atef, Sifeng Liu
      Abstract: The objective of this paper is to formulate the precise meanings of grey graphs and examine some of their properties. This article introduces innovative concepts of grey sets based on the grey number. We establish the grey graphs and examine their essential properties as isomorphisms of these graphs. Additionally, we explore the notion of a grey-complete graph and demonstrate certain properties of self-complementary grey-complete graphs. We showcase novel facets of grey system theory through the establishment of the structures of grey graphs, and the subsequent analysis of their distinctive traits. This article provides us with a new theoretical direction for grey system theory according to grey numbers. Thus, we present test examples that explain the routes between cities and the electrical wires between homes. Furthermore, the concept of grey graphs can be applied in several areas of engineering, computer science, neural networks, artificial intelligence, and medical diagnosis. The proposed concepts are considered novel mathematical directions in grey system theory for the first time. Some operations of grey graphs are also explored.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-06-27
      DOI: 10.1108/GS-12-2023-0125
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Grey multi-criteria group consensus decision-making based on cobweb model

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      Authors: Sandang Guo, Liuzhen Guan, Qian Li, Jing Jia
      Abstract: Considering the bounded confidence of decision-makers (DMs), a new grey multi-criteria group consensus decision-making (GMCGCDM) model is established by using interval grey number (IGN), cobweb model, social network analysis (SNA) and consensus reaching process (CPR). Firstly, the model analyzes the social relationship of DM under social networks and proposes a calculation method for DMs’ weights based on SNA. Secondly, the model defines a cobweb model to consider the preferences of decision-making alternatives in the decision-making process. The consensus degree is calculated by the area surrounded by the connections between each index value of DMs and the group. Then, the model coordinates the different opinions of various DMs to reduce the degree of bias of each DM and designs a consensus feedback mechanism based on bounded confidence to guide DMs to reach consensus. The advantage of the proposed method is to highlight the practical application, taking the selection of low-carbon suppliers in the context of dual carbon as an example. Comparison analysis is performed to reveal the interpretability and applicability of the method. The main contribution of this paper is to propose a new GMCGCDM model, which can not only expand the calculation method of DM’s weight and consensus degree but also reduce the time and cost of decision-making.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-06-18
      DOI: 10.1108/GS-08-2023-0079
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • What kind of urban brand ecology attracts talent best' Grey configuration
           analysis of 98 Chinese cities

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      Authors: Zhaohu Dong, Peng Jiang, Zongli Dai, Rui Chi
      Abstract: Talent is a key resource for urban development, and building and disseminating urban brands have an important impact on attracting talent. This paper explores what kind of urban brand ecology (UBE) can effectively enhance urban talent attraction (UTA). We explore this question using a novel grey quantitative configuration analysis (GQCA) model. To develop the GQCA model, grey clustering is combined with qualitative configuration analysis (QCA). We conducted comparative configuration analysis of UTA using fuzzy set QCA (fsQCA) and the proposed GQCA. We find that the empirical results of fsQCA may contradict the facts, and that the proposed GQCA effectively solves this problem. Based on the theory of UBE, we identify bottleneck factors for improving UTA at different stages. Seven configuration paths are described for cities to enhance UTA. Theoretically, this study expands the application boundaries of UBE. The proposed GQCA effectively solves the problem of inconsistent analysis and facts caused by the use of a binary threshold by the fsQCA. In practical case studies, the GQCA significantly improves the reliability of configuration comparisons and the sensitivity of QCA to cases, demonstrating excellent research performance.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-06-10
      DOI: 10.1108/GS-03-2024-0035
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Symmetric Kullback–Leibler distance based generalized grey target
           decision method for mixed attributes

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      Authors: Jinshan Ma, Hongliang Zhu
      Abstract: The reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes. The decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative. The case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number. This work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-06-04
      DOI: 10.1108/GS-01-2024-0001
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Organizational culture’s influence on supply chain performance analysis
           with fuzzy grey cognitive maps

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      Authors: Lucas Gabriel Zanon, Tiago F.A.C. Sigahi, Rosley Anholon, Luiz Cesar Ribeiro Carpinetti
      Abstract: This paper applies fuzzy grey cognitive maps (FGCM) to support multicriteria group decision making (GDM) on supply chain performance (SCP) considering the role of organizational culture as a moderating factor. This paper follows the quantitative axiomatic prescriptive model-based research. It introduces a MGDM model that relies on the SCOR® model performance attributes and Hofstede’s cultural dimensions. The proposal is underpinned by the soft computing technique of FGCM, aimed at addressing the inherent subjectivity associated with evaluating the culture-performance relationship within supply chains. The FGCM-based model proposes a management matrix tool for supporting SPC management. It results in a graphical representation that deconstructs SCP and organizational culture into key elements and provides directives for action plans that align improvement efforts. An illustrative application is presented to guide and promote the model’s application in different configurations of supply chains. This model offers valuable insights into addressing the impact of organizational culture on decision-making related to SCP. Additionally, it facilitates scenario simulation. The management matrix visually illustrates how each performance attribute is influenced by each cultural dimension on a quantitative scale. It also ranks these attributes based on the overall level of influence they receive from culture. The study provides a unique outlook on the use of FGCMs to support the SCP decisional process by detailing and accounting for the influence of organizational culture. This is done through the development of a novel matrix that allows for visual management and benchmarking.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-06-04
      DOI: 10.1108/GS-10-2023-0099
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Forecasting hospital outpatient volume using an optimized medical
           two-stage hybrid grey model

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      Authors: Youyang Ren, Yuhong Wang, Lin Xia, Wei Liu, Ran Tao
      Abstract: Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers. This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor. This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models. The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-30
      DOI: 10.1108/GS-01-2024-0005
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Improving electricity demand forecasting accuracy: a novel grey-genetic
           programming approach using GMC(1,N) and residual sign estimation

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      Authors: Flavian Emmanuel Sapnken, Benjamin Salomon Diboma, Ali Khalili Tazehkandgheshlagh, Mohammed Hamaidi, Prosper Gopdjim Noumo, Yong Wang, Jean Gaston Tamba
      Abstract: This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation. The research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon. The novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R2, yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns. This paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-30
      DOI: 10.1108/GS-01-2024-0011
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • The grey decision model and its application based on generalized greyness
           of interval grey number

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      Authors: Li Li, Xican Li
      Abstract: In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number. Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model. The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers. The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers. The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-27
      DOI: 10.1108/GS-01-2024-0003
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • A novel fractional multivariate grey prediction model for forecasting
           hydroelectricity consumption

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      Authors: Ye Li, Hongtao Ren, Junjuan Liu
      Abstract: This study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting methods. This paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The Particle Swarm Optimization algorithm is then utilized to compute the model’s hyperparameters. Subsequently, the proposed model is applied to forecast China’s hydroelectricity consumption and is compared with other models for analysis. Theoretical derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model’s effectiveness in handling complex and nonlinear data, emphasizing its practical applicability. This paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China’s hydroelectricity resource development scheduling and planning. The primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-23
      DOI: 10.1108/GS-09-2023-0095
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Predicting of aging population density by a hybrid grey exponential
           smoothing model (HGESM): a case study from Sri Lanka

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      Authors: R.M. Kapila Tharanga Rathnayaka, D.M.K.N. Seneviratna
      Abstract: The global population has been experiencing an unprecedentedly rapid demographic transition as the populations have been growing older in many countries during the current decades. The purpose of this study is to introduce a Grey Exponential Smoothing model (GESM)-based mechanism for analyzing population aging. To analyze the aging population of Sri Lanka, initially, three major indicators were considered, i.e. total population, aged population and proportion of the aged population to reflect the aging status of a country. Based on the latest development of computational intelligence with Grey techniques, this study aims to develop a new analytical model for the analysis of the challenge of disabled and frail older people in an aging society. The results suggested that a well-defined exponential trend has been seen for the population ages 65 and above, a total of a million) during 1960–2022; especially, the aging population ages 65 and above has been rising rapidly since 2008. This will increase to 24.8% in 2040 and represents the third highest percentage of elderly citizens living in an Asian country. By 2041, one in every four Sri Lankans is expected to be elderly. The study proposed a GESM-based mechanism for analyzing the population aging in Sri Lanka based on the data from 1960 to 2022 and forecast the aging demands in the next five years from 2024 to 2028.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-16
      DOI: 10.1108/GS-01-2024-0002
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Prioritization of emergency assembly points in a campus using grey
           p-median linear programming model

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      Authors: Damla Yalçıner Çal, Erdal Aydemir
      Abstract: The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times. Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students. In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios. It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters. Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-14
      DOI: 10.1108/GS-12-2023-0120
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Resilience evaluation model of photovoltaic industry chain based on
           grey-entropy-catastrophe progression method: a case study of Jiangsu
           province

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      Authors: Lan Xu, Yaofei Wang
      Abstract: The purpose of this study is to establish a grey-entropy-catastrophe progression method (CPM) model to assess the photovoltaic (PV) industry chain resilience of Jiangsu Province in China. First, we designed the resilience evaluation index system of such a chain from two aspects: the external environment and internal conditions. We then constructed a PV industry chain resilience evaluation model based on the grey-entropy-CPM. Finally, the feasibility and applicability of the proposed model were verified via an empirical case study analysis of Jiangsu Province in China. As of the end of 2022, the resilience level of its PV industry chain is medium-high resilience, which indicates a high degree of adaptability to the current unpredictable and competitive market, and can respond to the uncertain impact of changes in conditions effectively and in a timely manner. The construction of this model can provide reference ideas for related enterprises in the PV industry to analyze the resilience level of the industrial chain and solve the problem of industrial chain resilience. Firstly, an analysis of the entire industrial chain structure of the PV industry, combined with its unique characteristics is needed to design a PV industry chain resilience evaluation index system. Second, grey relational analysis (GRA) and the entropy method were adopted to improve the importance of ranking the indicators in the evaluation of the CPM, and a resilience evaluation model based on grey-entropy-CPM was constructed.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-13
      DOI: 10.1108/GS-09-2023-0085
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Damping accumulative NDAGM(1,N, ) power model and its applications

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      Authors: Ye Li, Chengyun Wang, Junjuan Liu
      Abstract: In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems. Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models. By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues. This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export. The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-10
      DOI: 10.1108/GS-12-2023-0117
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Equivalence class of complete correlation determination of several gray
           incidence degrees

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      Authors: Yong Wei, Shasha Xi
      Abstract: This paper sets out to solve a common and crucial fundamental theoretical problem of gray incidence cluster analysis: to [X]={X ρ(X,Y)≥1−ε0} constitute an approximate classification, it must first be proven that [X]={X ρ(X,Y)=1} constitutes a rigorous classification. This paper does not study the concrete expressions of various incidence degrees but rather the perfect correlation essence of such incidence degrees, that is, sufficient and necessary conditions. For any order difference incidence degree, the similarity incidence degree, the direct proportion incidence degree, the parallel incidence degree and the nearness incidence degree, it is proven that the perfect correlation relation is an equivalence relation. The set composed of all sequences Y that are equivalent to sequences X is studied, that is, the equivalence class of X. The structure and mutual relations of these equivalence classes are discussed, and the topological homeomorphism concept of incidence degree is introduced. The conclusion is obtained that the equivalence classes of the two incidence degrees must be the same when the topological homeomorphism is obtained. In this paper, only the perfect correlation relation of any order difference incidence degree, the similarity incidence degree, the direct proportion incidence degree, the parallel incidence degree and the nearness incidence degree are studied as equivalent relations. Not only are the research results of several incidence degrees involved in this paper original but also many other effective incidence degrees have not done this basic research, so this paper opens up a research direction with theoretical significance.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-09
      DOI: 10.1108/GS-12-2023-0119
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • Hyperspectral estimation model of soil organic matter content based on
           principal gradient grey information

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      Authors: Lu Xu, Shuang Cao, Xican Li
      Abstract: In order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory. Firstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province. The results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective. The estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on. The paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-05-08
      DOI: 10.1108/GS-12-2023-0124
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • A novel grey forecasting model with generalised fractal derivative and its
           optimisation

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      Authors: Lina Jia, MingYong Pang
      Abstract: The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy. The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models. The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model. The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance. The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-04-30
      DOI: 10.1108/GS-11-2023-0109
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • A novel time-varying grey Fourier model for variable amplitude seasonal
           fluctuation sequences

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      Authors: Xiaomei Liu, Bin Ma, Meina Gao, Lin Chen
      Abstract: A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well. The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision. The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPEF and MAPET are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR). The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-03-27
      DOI: 10.1108/GS-10-2023-0101
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
  • A novel fractional order variable structure multivariable grey prediction
           model with optimal differential background-value coefficients and its
           performance comparison analysis

    • Free pre-print version: Loading...

      Authors: Chao Xia, Bo Zeng, Yingjie Yang
      Abstract: Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance. A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance. The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models. This study has positive implications for enriching the method system of multivariable grey prediction model.
      Citation: Grey Systems: Theory and Application
      PubDate: 2024-02-09
      DOI: 10.1108/GS-08-2023-0082
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
       
 
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