Method for measuring difference degree between plural evidence theory models
By transforming the complex evidence theory model into a complex-valued distribution and mapping it to the real number domain, and using KL divergence to calculate the differences between complex evidence models, the problem of measurement difficulties in the traditional method for complex evidence theory models is solved, and accurate measurement and analysis in the real number domain is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-04-03
- Publication Date
- 2026-04-10
AI Technical Summary
Traditional evidence theory cannot effectively measure the degree of difference between complex evidence theory models, especially when the data changes periodically, it is inaccurate and ambiguous. The existing KL divergence method is only applicable to probability distribution models and cannot be applied to complex evidence theory models.
The complex evidence theory model is transformed into a complex-valued distribution model and mapped to the real number domain through the CBEt and RBEt functions. The degree of difference between complex evidence models is calculated using KL divergence, specifically including the basic probability assignment of complex numbers, the probability distribution of complex values, and the KL divergence calculation in the real number domain.
It enables accurate measurement of the degree of difference between complex evidence theory models in the real number domain, which facilitates understanding and analysis and provides a method for measuring differences in practical applications.
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Figure CN121834635A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of metric complex number correlation, and particularly relates to a method for measuring the difference degree between metric complex number evidence theory models. BACKGROUND
[0002] Traditional evidence theory cannot express periodic and regular data. Complex evidence theory allows to explain data with periodic changes. When data changes periodically, complex numbers can indicate inevitable inaccuracies and ambiguities. However, there is no clear solution to the problem of measuring the difference degree between two complex evidence theory models.
[0003] The existing KL divergence difference measurement can only be applied to probability distribution models. Through the method, the complex evidence theory model is converted into a complex value distribution and mapped to the real number field, and then the KL divergence is calculated in the real number field to measure the difference between two complex evidence theory models. SUMMARY
[0004] The application aims to provide a method for measuring the difference degree between metric complex number evidence theory models to solve the problems in the background.
[0005] To achieve the above purpose, the application provides the following technical solutions.
[0006] A method for measuring the difference degree between metric complex number evidence theory models, the method comprising a complex evidence model, a complex value distribution model, a probability distribution model and calculating KL divergence, the complex evidence model comprising a complex basic probability assignment, the complex value distribution model comprising a complex value probability distribution, the probability distribution model comprising a probability distribution, and the calculating KL divergence mainly calculating and the KL divergence of RBet2 and and RBet1.
[0007] The complex evidence model has two, and the two complex evidence theory models are defined as follows.
[0008]
[0009]
[0010] The CBet function is used to convert it into a complex value distribution
[0011]
[0012]
[0013] The Rbet function is used to map the complex value distribution to the real number field
[0014]
[0015]
[0016] Computing KL divergence by real number field mapping
[0017]
[0018]
[0019] Compared with the prior art, the present application provides a method for measuring the difference degree between complex evidence theory models, which has the following beneficial effects:
[0020] Through the proposed mapping method, the complex evidence theory model can be mapped to a single distribution model in the real number field, facilitating the understanding and analysis of the complex evidence theory model in the real number field.
[0021] By calculating the KL divergence in the real number field, the model can measure the difference degree between two complex evidence theory models, and this difference degree can be further applied in practice. BRIEF DESCRIPTION OF DRAWINGS
[0022] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application and explain the technical solutions of the present application, and do not constitute a limitation on the present application, and in the drawings:
[0023] Figure 1 A flowchart of a method for measuring the difference degree between complex evidence theory models is provided. DETAILED DESCRIPTION
[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.
[0025] Please refer to Figure 1 The present application provides a technical solution:
[0026] A method for measuring the difference degree between complex evidence theory models, the method comprising a complex evidence model, a complex value distribution model, a probability distribution model and calculating KL divergence, the complex evidence model comprising a complex basic probability assignment, the complex value distribution model comprising a complex value probability distribution, the probability distribution model comprising a probability distribution, and the calculating KL divergence mainly calculating and RBet2 and KL divergence with RBet1.
[0027] Two complex evidence models are shown below:
[0028]
[0029]
[0030] Transform to complex valued distribution via CBet function
[0031]
[0032]
[0033]
[0034] Map complex valued distribution to real valued domain via Rbet function
[0035]
[0036]
[0037] Compute KL divergence via real valued domain mapping
[0038]
[0039]
[0040] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary and that changes in form and detail can be made without departing from the spirit and scope of the application, which is defined by the following claims and their equivalents.
Claims
1. A method for measuring the degree of difference between complex evidence theory models, the method comprising complex evidence models, complex-valued distribution models, probability distribution models, and calculating KL divergence, characterized in that: The complex evidence model includes a complex fundamental probability assignment, the complex-valued distribution model includes a complex-valued probability distribution, the probability distribution model includes a probability distribution, and the calculation of the KL divergence mainly involves calculating... and RBet2 and KL divergence between RBet1 and RBet1.
2. The method for measuring the degree of difference between complex evidence theory models according to claim 1, characterized in that: There are two complex evidence models, and the two complex evidence theoretical models are defined as follows: M1:IM1({θ1})=0.66708e iarctan(0.5000) , IM1({θ2})=0, IM1({θ3})=0.2236e iarctan(-2.0000) , IM1({θ1,θ2})=0.1414e iarctan(1.0000) , IM1({θ1,θ3})=0.2828e iarctan(-1.0000) , IM1{{θ2,θ3}}=0, IM1({θ1, θ2, θ3}) = 0, IM2:IM2({θ1})=0.5385e iarctan(-0.4000) , IM2({θ2})=0.2236e iarctan(-2.0000) , IM2({θ3})=0.2236e iarctan(0.5000) , IM2({θ1, θ2}) = 0, IM2({θ1,θ3})=0.1414e iarctan(1.0000) , IM2({θ2, θ3}) = 0, IM2({θ1,θ2,θ3})=0.2236e iarctan(2.0000) . Transform it into a complex-valued distribution using the CBeet function. IM1: IM2: The Rbet function maps complex-valued distributions to the real number domain. IM1: IM2: Calculate KL divergence using a real-field mapping