Performance Index of Incremental Granular Model with Information Granule of Linguistic Intervals and Its Application

Abstract

This paper addresses the performance index (PI) of an incremental granular model (IGM) with information granules of linguistic intervals. For this purpose, IGM is designed by combining a linear regression (LR) and an interval-based granular model (GM). The fundamental scheme of IGM construction comprises two essential phases: (1) development of LR as a basic model and (2) design of a local granular model, which attempts to reduce errors obtained by the LR model. Here, the local interval-based GM is based on an interval-based fuzzy clustering algorithm, which is materialized by information granulations. The PI of IGM is calculated by multiplying the coverage with specificity property, because the output of IGM is not a numerical value but a linguistic interval value. From the concept of coverage and specificity, we can construct information granules; thus, it is justified by the available experimental proof and presented as clearly defined semantics. To validate the PI method, an experiment is conducted on concrete compressive strength for civil engineering applications. The experimental results confirm that the PI of IGM is an effective performance evaluation method.