Constructing the regional intelligent economic decision support system based on fuzzy C-mean clustering algorithm

Wang, Jia; Yu, Chuangang; Zhang, Juying

doi:10.1007/s00500-019-04091-3

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…It has an important role in the economy and believes that the impact of import and export trade on economic development is mainly reflected in the quality of economic growth and structural transformation and upgrading; it is reflected in maintaining the balance of international payments and maintaining the stability of the RMB exchange rate. Wang et al [10] believe that when examining the effect of import and export trade on economic growth, both the net export analysis method and the total export volume method have limitations in handling extreme imports. ey are not as reasonable and objective as the import decomposition analysis method, and the import and export data from 2000 to 2010 were verified, and the following conclusions were drawn: when import and export trade grew rapidly and exports grew faster than imports, import and export trade had a significant positive effect on economic growth; while import and export trade grew when imports are relatively slow and imports grow faster than exports, import and export trade will have a negative effect on economic growth.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Foreign Trade Export Forecast Based on Fuzzy Neural Network

Liu

2021

Complexity

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This article first analyzes the significance and methods of foreign trade export forecasting and determines the index system of foreign trade export forecasting by analyzing the results of foreign trade export forecasting research at home and abroad. Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural network and fuzzy theory are combined to establish the prediction model. Finally, according to the characteristics of foreign trade exports, this article comprehensively considers the influence of various factors, applies the fuzzy neural network model to the foreign trade export forecast, introduces the whole process of the establishment of the fuzzy neural network forecasting model in detail, and predicts the change interval of foreign trade exports.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Foreign Trade Export Forecast Based on Fuzzy Neural Network

Liu

2021

Complexity

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“…That is, the urgent issue today is development of methods and models that would create methodological basis for carrying out research relevant to development of socioeconomic systems in conditions of uncertainty [15][16][17][18], building forecasts and recommendations for decision-making [19][20][21], forming an effective mechanism for managing their functioning [22][23][24][25], and determining the prospects for their short-and long-term development [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

The improvement of the intelligent decision support system for forecasting non-linear non-stationary processes

Bidiuk,

Prosyankina-Zharova,

Diakon

et al. 2023

TAPR

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The paper is focused on solving the modern scientific and applied problem related to development and practical use in Decision Support Systems (DSS) of information technologies directed towards forecasting of non-linear non-stationary processes (NNP) that take place in economy and finances as well as in many other areas of activities. Thus, object of study are non-linear non-stationary processes taking place in economy and financial sphere. The basic problem of the study is development of new mathematical models and methods of analysis and forecasting non-linear non-stationary processes in economy and finances, improvement of information decision support technologies that would help to enhance quality of forecast estimates and respective decisions in conditions of uncertainties and risk. The methods given in the paper are used for automating the process of intellectual data analysis that describe the processes under study and automatizing model constructing procedures. As a result of the study performed the information technology was developed to be used in DSS based upon system analysis principles, taking into consideration possible data uncertainties, regression and intellectual data analysis. The technology provides for constructing adequate models of the process under study and computing high quality forecast estimates. The particular feature of the approach proposed is that it provides for high quality of experimental results due to taking into consideration special features of non-linear non-stationary processes that take place in various spheres of activities and their evolution is influenced by many specific factors. The use of the technology proposed in decision support systems of enterprises, state governmental organs, and local self-government will create basis for effective solving the tasks of governing development of non-linear non-stationary processes that take place in many spheres of activities. The approaches proposed in the paper can be used in practice as separately as well as parts of existing information systems at enterprises and organizations.

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“…In [12], the authors proposed a class of spatial econometric methods in spatial clustering of industries in the space. Recently, an economic decision support system based on fuzzy C-mean clustering is proposed in [13] for economic forecasting. A hybrid algorithm is presented in [14] for spatial small area estimation to use in agricultural and resource economics surveys.…”

Section: Introductionmentioning

confidence: 99%

A Markov Chain Monte Carlo Algorithm for Spatial Segmentation

Raveendran

Sofronov

2021

Information

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Spatial data are very often heterogeneous, which indicates that there may not be a unique simple statistical model describing the data. To overcome this issue, the data can be segmented into a number of homogeneous regions (or domains). Identifying these domains is one of the important problems in spatial data analysis. Spatial segmentation is used in many different fields including epidemiology, criminology, ecology, and economics. To solve this clustering problem, we propose to use the change-point methodology. In this paper, we develop a new spatial segmentation algorithm within the framework of the generalized Gibbs sampler. We estimate the average surface profile of binary spatial data observed over a two-dimensional regular lattice. We illustrate the performance of the proposed algorithm with examples using artificially generated and real data sets.

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Constructing the regional intelligent economic decision support system based on fuzzy C-mean clustering algorithm

Cited by 9 publications

References 17 publications

[Retracted] Foreign Trade Export Forecast Based on Fuzzy Neural Network

[Retracted] Foreign Trade Export Forecast Based on Fuzzy Neural Network

The improvement of the intelligent decision support system for forecasting non-linear non-stationary processes

A Markov Chain Monte Carlo Algorithm for Spatial Segmentation

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