Information Structures in an Incomplete Interval-Valued Information System

Zeng, Jiasheng; Li, Zhaowen; Li, Meng; Liao, Shimin

doi:10.2991/ijcis.d.190712.001

Cited by 2 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed methodology can also be applied to other learning processes in various fields that are subject to uncertainty, such as unit cost learning [65] and energy efficiency learning [66]. Another interesting topic is how to build the IFN-based fuzzy productivity learning model if the collected productivity data are incomplete [67]. The proposed methodology can also be extended to fulfill a multiple-expert collaborative forecasting task [68][69][70][71][72].…”

Section: Discussionmentioning

confidence: 99%

Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

2020

View full text Add to dashboard Cite

Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)–ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory.

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

2020

View full text Add to dashboard Cite

show abstract

“…In this section, we make a comparison with literatures [11,23] so as to see the innovation of this article more clearly.…”

Section: Comparisonsmentioning

confidence: 99%

“…(a) The studied ISs are different: This article considers i-structure in an OIS, literature [11] studies i-structure in a covering IS and literature [23] investigates i-structure in an incomplete interval-valued IS.…”

Section: Comparisonsmentioning

confidence: 99%

“…(b) The introduced relations are different: This article introduces the dominance relation on the object set in an OIS by defining the order between two information values, literature [11] proposes the similarity relation on the universe in a covering IS by means of the neighborhood of each pointand literature [23] presents the tolerance relation on the object set in an incomplete interval-valued IS by defining the similarity degree between two information values.…”

Section: Comparisonsmentioning

confidence: 99%

“…a covering IS and literature [23] constructs an i-granule by means of the tolerance class on each object in an incomplete interval-valued IS.…”

Section: Characterizations Invariantmentioning

confidence: 99%

See 2 more Smart Citations

Information Structures in an Ordered Information System Under Granular Computing View and Their Optimal Selection Based on Uncertainty Measures

Wang¹,

Wang²,

Tang³

2020

IJCIS

View full text Add to dashboard Cite

Information structures (i-structures) in an ordered information system (OIS) are mathematical structures of the information granules (i-granules) granulated from the data set of this OIS. This article investigates i-structures in an OIS with granular computing (GrC) view, i.e., i-structures in an OIS are seen as granular structures. Dependence and independence between istructures are first depicted in the same OIS. Then, information distance (i-distance) between i-structures in the same OIS are proposed, and information entropy in an OIS can be expressed by i-distance between i-structures in this OIS is proved. Next, properties of i-structures in an OIS are shown. Moreover, group, lattice and map characters of i-structures in an OIS are received, and some invariant characters of i-structures in an OIS under homomorphisms are obtained. Finally, the optimal selection of i-structures in an OIS based on the proposed measures are studied. These results will contribute to build a framework of GrC in an OIS.

show abstract

Information Structures in an Incomplete Interval-Valued Information System

Cited by 2 publications

References 28 publications

Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

Information Structures in an Ordered Information System Under Granular Computing View and Their Optimal Selection Based on Uncertainty Measures

Contact Info

Product

Resources

About