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Article
Publication date: 6 February 2017

Yi-Chung Hu

Energy demand is an important economic index, and demand forecasting has a significant role when devising energy development plans for cities or countries. GM(1,1) model…

Abstract

Purpose

Energy demand is an important economic index, and demand forecasting has a significant role when devising energy development plans for cities or countries. GM(1,1) model has become popular because it needs only a few data points to construct a time-series model without statistical assumptions. Several methods have been developed to improve prediction accuracy of the original GM(1,1) model by only estimating the sign of each residual. This study aims to address that this is too tight a restriction for the modification range.

Design/methodology/approach

Based on the predicted residual, this study uses the functional-link net (FLN) with genetic-algorithm-based learning to estimate the modification range for its corresponding predicted value obtained from the original GM(1,1) model.

Findings

The forecasting ability of the proposed grey prediction model is verified using real energy demand cases from China. Experimental results show that the proposed prediction model performs well compared to other grey residual modification models with sign estimation.

Originality/value

The proposed FLNGM(1,1) model can improve prediction accuracy of the original GM(1,1) model using residual modification. The distinctive feature of the proposed model is to use an FLN to estimate sign and modification range simultaneously for the predicted value based on its corresponding predicted residual obtained from the residual GM(1,1) model.

Details

Kybernetes, vol. 46 no. 2
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 21 June 2019

Hang Jiang, Yi-Chung Hu, Jan-Yan Lin and Peng Jiang

With the development of economy, China’s OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological…

Abstract

Purpose

With the development of economy, China’s OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological development of home country. Thus, accurate OFDI prediction is a prerequisite for the effective development of international investment strategies. The purpose of this paper is to predict China’s OFDI accurately using a novel multivariable grey prediction model with Fourier series.

Design/methodology/approach

This paper applied a multivariable grey prediction model, GM(1,N), to forecast China’s OFDI. In order to improve the prediction accuracy and without changing local characteristics of grey model prediction, this paper proposed a novel grey prediction model to improve the performance of the traditional GM(1,N) model by combining with residual modification model using GM(1,1) model and Fourier series.

Findings

The coefficients indicate that the export and GDP have positive influence on China’s OFDI, and, according to the prediction result, China’s OFDI shows a growing trend in next five years.

Originality/value

This paper proposed an effective multivariable grey prediction model that combined the traditional GM(1,N) model with a residual modification model in order to predict China’s OFDI. Accurate forecasting of OFDI provides reference for the Chinese Government to implement international investment strategies.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 8 July 2020

Peng Jiang, Wenbao Wang, Yi-Chung Hu, Yu-Jing Chiu and Shu-Ju Tsao

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple…

Abstract

Purpose

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).

Design/methodology/approach

For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.

Findings

The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.

Practical implications

In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.

Originality/value

This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.

Details

Grey Systems: Theory and Application, vol. 11 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

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Article
Publication date: 18 June 2020

Yi-Chung Hu, Peng Jiang, Hang Jiang and Jung-Fa Tsai

In the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms…

Abstract

Purpose

In the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).

Design/methodology/approach

As the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).

Findings

Experimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.

Practical implications

Among artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.

Originality/value

Applying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

Details

Grey Systems: Theory and Application, vol. 11 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

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Article
Publication date: 30 December 2019

Quynh-Trang Nguyen, Ming-Yen Lee and Yi-Chung Hu

This study aims to concentrate on a specific perspective that has mostly been ignored: employees in social enterprises (SEs). It proposes that employees in SEs should be…

Abstract

Purpose

This study aims to concentrate on a specific perspective that has mostly been ignored: employees in social enterprises (SEs). It proposes that employees in SEs should be treated with equal importance to outside beneficiaries within the SEs’ value-creating mission.

Design/methodology/approach

A multiple case study approach is adopted, and semi-structured interviews are the primary means of data collection.

Findings

The results show that while economic values are fundamental for the employment relationship, social values play the leading role in employees’ motivation; thus, they can significantly affect the organization’s operation and development.

Research limitations/implications

This work contributes to Maslow’s need theory and psychological contract theory regarding their application to SEs. Practical lessons and suggestions are also provided for SEs’ development.

Originality/value

By emphasizing the value-creating mission of SEs with the new perspective of including employees in it, this work provides empirical evidence and practical lessons for SEs, especially Asian SEs, in terms of management and strategy.

Details

Social Enterprise Journal, vol. 16 no. 1
Type: Research Article
ISSN: 1750-8614

Keywords

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Article
Publication date: 1 February 2016

Yi-Chung Hu

– The purpose of this paper is to propose that the grey tolerance rough set (GTRS) and construct the GTRS-based classifiers.

Abstract

Purpose

The purpose of this paper is to propose that the grey tolerance rough set (GTRS) and construct the GTRS-based classifiers.

Design/methodology/approach

The authors use grey relational analysis to implement a relationship-based similarity measure for tolerance rough sets.

Findings

The proposed classification method has been tested on several real-world data sets. Its classification performance is comparable to that of other rough-set-based methods.

Originality/value

The authors design a variant of a similarity measure which can be used to estimate the relationship between any two patterns, such that the closer the relationship, the greater the similarity will be.

Details

Kybernetes, vol. 45 no. 2
Type: Research Article
ISSN: 0368-492X

Keywords

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