Uncertainty measure in evidence theory

Deng, Yong

doi:10.1007/s11432-020-3006-9

Cited by 268 publications

(108 citation statements)

References 117 publications

(149 reference statements)

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“…e SNPS-based fault diagnosis methods (for example, the ones for power systems) are derived from the similarities between the pulse transmission between neurons via synapses and the fault propagation in power systems. Accordingly, the basic mechanism to address fault diagnosis based on SNPSs is to find faulty sections by dealing with the uncertainty [35] of fault alarm information. In general, the input neurons of an SNPS correspond to protective devices (including protective relays and circuit breakers), and the output neurons are associated with suspicious fault sections.…”

Section: Introductionmentioning

confidence: 99%

A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems

et al. 2021

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The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems

et al. 2021

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“…Meanwhile, the DRC meets commutative and associative laws [16,17]. Hence, DSET has been extensively researched, including the aspects of D numbers [18,19], evidential reasoning [20], heuristic representation learning [21], entropy [22,23], generation [24,25], dependency [26], the negation [27] of BBAs, etc. [12].…”

Section: Introductionmentioning

confidence: 99%

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Xiao

2021

Sensors

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Multisource information fusion has received much attention in the past few decades, especially for the smart Internet of Things (IoT). Because of the impacts of devices, the external environment, and communication problems, the collected information may be uncertain, imprecise, or even conflicting. How to handle such kinds of uncertainty is still an open issue. Complex evidence theory (CET) is effective at disposing of uncertainty problems in the multisource information fusion of the IoT. In CET, however, how to measure the distance among complex basis belief assignments (CBBAs) to manage conflict is still an open issue, which is a benefit for improving the performance in the fusion process of the IoT. In this paper, therefore, a complex Pignistic transformation function is first proposed to transform the complex mass function; then, a generalized betting commitment-based distance (BCD) is proposed to measure the difference among CBBAs in CET. The proposed BCD is a generalized model to offer more capacity for measuring the difference among CBBAs. Additionally, other properties of the BCD are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. Besides, a basis algorithm and its weighted extension for multi-attribute decision-making are designed based on the newly defined BCD. Finally, these decision-making algorithms are applied to cope with the medical diagnosis problem under the smart IoT environment to reveal their effectiveness.

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“…Moreover, multi-source information can be fused without a prior probability distribution [17]. The D-S theory offers a mathematical framework for uncertainty modeling [18]. Moreover, the D-S theory can decrease the degree of uncertainty [19].…”

Section: Introductionmentioning

confidence: 99%

Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

et al. 2021

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This study focuses on the reentry target classification and fuses target features based on the generalized evidence theory. The features are extensively investigated, and the ballistic factor and length of the high-resolution range profile are selected. The evidence theory is advantageous for solving feature fusion, representing uncertainty, and is widely used in defense applications. However, determining the generalized basic probability assignment (GBPA) and dealing with uncertainty is a matter that requires further improvement. In this paper, we propose a new method to determine GBPA using uncertainty with time-series radar data. First, the samples of each known class are encoded as a generalized fuzzy number (GFN), and the power set comprising the frame of discernment (FOD) is calculated from the GFN and each intersection area. Subsequently, the test samples with uncertainty are encoded as triangular fuzzy numbers, reflecting the mean and standard deviation of a Kalman filter. Finally, the firing strength between the model and the input is calculated as the degree of support for the class hypothesis, which is used to determine the GBPA. The proposed algorithm is compared with the existing methods and exhibits high classification accuracy and a short classification time without leakage. In experiments with various input uncertainties, the results demonstrate that our method can effectively reflect the input uncertainty and determine the GBPA.

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Uncertainty measure in evidence theory

Cited by 268 publications

References 117 publications

A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems

A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

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