Multi-Attribute Fusion Algorithm Based on Improved Evidence Theory and Clustering

Abstract

In most of the application scenarios of industrial control systems, the switching threshold of a device, such as a street light system, is typically set to a fixed value. To meet the requirements for a smart city, it is necessary to set a threshold that is adaptive to different conditions by fusing the multi-attribute observations of the sensors. This paper proposes a multi-attribute fusion algorithm based on fuzzy clustering and improved evidence theory. All of the observations are clustered by fuzzy clustering, where a proper clustering method is chosen, and the improved evidence theory is used to fuse the observations. In the experiments, two-dimensional observations for the street light illumination and for the ambient illumination are used in a campus-intelligent lighting system based on a narrowband Internet of things, and the results demonstrate the effectiveness of the proposed fusion algorithm. The proposed algorithm can be applied to a variety of multi-attribute fusion scenarios.

Multi-Sensor Data Fusion Method Based on Improved Evidence Theory

“…Since the proposed method is universal, the fusion performance of experimental data from other aspects can also reflect the multi-sensor information fusion performance of unmanned surface vehicles. The feasibility and effectiveness of the proposed method and its superiority over other fusion methods were verified by testing all fusion algorithms on the measurement data of two multi-sensor experiments provided by [28] and the measurement data of one multi-sensor experiment provided by [40].…”

“…To realize the fusion of multi-sensor measurements, a new multi-sensor data fusion method [28] is proposed to convert each measurement into the corresponding evidence according to its accuracy. Wang et al [29] extended the method of [28] to propose a multiple-attribute fusion algorithm combining improved evidence theory and fuzzy clustering, which uses a fuzzy clustering approach to cluster and group measurements and then uses improved evidence theory for fusion. However, this method requires a large amount of data to achieve the fusion accuracy and is not applicable in the case of a small number of sensors.…”

Investment Decision of New Energy Vehicle Enterprises Using the Interval Basic Probability Assignment-Based Intuitionistic Fuzzy Set

“…According to formula (18), the orthogonal sum operation M 1 ⊕ M 2 of intuitionistic fuzzy sets M 1 and M 2 can be expressed as…”

“…D-S evidence theory is an imprecise reasoning theory established and perfected by Dempster and Shafer [14,15]. After nearly half a century of development, D-S evidence theory has become an important information fusion tool, with significant application effects in the fields of pattern recognition [16], decision analysis [17], clustering combination [18], and so on. In D-S evidence theory, the basic probability distribution function (BPA) focuses on the basic probability mass (BPM) of all focal elements to determine the exact BPA of the real number.…”