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System combination model credibility intelligent evaluation method based on deep learning

A technology of deep learning and combined models, applied in the field of system combat simulation modeling, can solve the problem of low reliability of functional-level equipment models, achieve adaptive intelligent evaluation and model screening, avoid fuzzy phenomena, and improve accuracy Effect

Active Publication Date: 2020-03-17
江南机电设计研究所
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Problems solved by technology

[0004] In order to solve the above-mentioned technical problems, the present invention provides an intelligent evaluation method for the credibility of system combination models based on deep learning, which solves the problem of the reliability of functional-level equipment models. For low-level problems, apply deep learning methods and adaptive optimization methods to convert the credibility of the combined model into the associated probability problem of feature representation, and realize the intelligent evaluation of the credibility of the combined model. While improving the evaluation efficiency, reduce the Uncertainty of the combined model and the influence of noise factors

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Embodiment

[0034] As mentioned above, for the radar system model in the context of system operations, the credibility evaluation process of the radar combination model based on the deep learning method is given in the context of cloud simulation. The specific implementation method is as follows:

[0035] Step 1: Obtain a reference learning sample set: take the verified signal-level radar model or radar simulator as a reference model, if m different input conditions are given, a reference learning sample set containing m learning samples can be obtained, Each learning sample contains p outputs;

[0036] Step 2: Obtain a simulation learning sample set: similar to step 1, for radar function-level models of different granularities obtained from the cloud, if n different input conditions are given, a simulation learning sample set containing n learning samples can be obtained ;

[0037] Step 3: Input the reference learning sample set and simulation learning sample set into the deep learning ...

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Abstract

The invention provides a system combination model credibility intelligent evaluation method based on deep learning. The system combination model credibility intelligent evaluation method comprises thefollowing steps of obtaining a reference learning sample set, obtaining the reference learning sample set, evaluating uncertainty influence degree, evaluating noise influence degree and evaluating credibility. According to the invention, a deep learning method is applied; uncertainty and influence of noise are comprehensively considered; the credibility evaluation of the to-be-inspected model ismore reliable; a reverse mapping relationship from a simulation learning sample (to-be-inspected model) is applied to a reference learning sample (reference model); a fuzzy phenomenon possibly occurring during forward mapping is avoided, credibility evaluation accuracy is improved, through the deep learning method and the optimization model based on the loss function, implementation difficulty ofsystem combat simulation credibility evaluation is reduced, and adaptive intelligent evaluation and model screening are realized.

Description

technical field [0001] The invention relates to a method for intelligently evaluating the credibility of a system combination model based on deep learning, and belongs to the technical field of system combat simulation modeling. Background technique [0002] As the battlefield environment and confrontation game behavior become more and more complex and changeable, modeling and simulation are becoming more and more important in the research of complex large systems and complex systems, but it also makes it difficult to quantitatively analyze, calculate and evaluate the reliability of system combat simulation. Especially in the pre-concept demonstration and development stages of systems and complex systems, in order to meet the rapid iteration of the scheme, most of them adopt coarse-grained and functional-level simulation models that are oriented to combat tasks and mainly focus on design indicators, so that the credibility of the simulation results How to construct a high-re...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F111/10
Inventor 李兴国廖咏一李延超罗德智杨荣强汪正东王海星彭芳
Owner 江南机电设计研究所
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