首页|Accountable capability improvement based on interpretable capability evaluation using belief rule base

Accountable capability improvement based on interpretable capability evaluation using belief rule base

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A new approach is proposed in this study for accountable capability improvement based on interpretable capability evaluation using the belief rule base(BRB).Firstly,a capability evaluation model is constructed and optimized.Then,the key sub-capabilities are identified by quantitatively calculat-ing the contributions made by each sub-capability to the overall capability.Finally,the overall capability is improved by optimiz-ing the identified key sub-capabilities.The theoretical contribu-tions of the proposed approach are as follows.(i)An inter-pretable capability evaluation model is constructed by employ-ing BRB which can provide complete access to decision-ma-kers.(ii)Key sub-capabilities are identified according to the quantitative contribution analysis results.(iii)Accountable capa-bility improvement is carried out by only optimizing the identi-fied key sub-capabilities.Case study results show that"Surveil-lance","Positioning",and"Identification"are identified as key sub-capabilities with a summed contribution of 75.55%in an analytical and deducible fashion based on the interpretable capability evaluation model.As a result,the overall capability is improved by optimizing only the identified key sub-capabilities.The overall capability can be greatly improved from 59.20%to 81.80%with a minimum cost of 397.Furthermore,this paper also investigates how optimizing the BRB with more collected data would affect the evaluation results:only optimizing"Surveil-lance"and"Positioning"can also improve the overall capability to 81.34%with a cost of 370,which thus validates the efficiency of the proposed approach.

accountable capability improvementinterpretable capability evaluationbelief rule base(BRB)

LI Xuan、JIANG Jiang、SUN Jianbin、YU Haiyue、CHANG Leilei

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College of Systems Engineering,National University of Defense Technology,Changsha 410073,China

China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing,Hangzhou Dianzi University,Hangzhou 310018,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Provincial Universities of Zhejiang

724710677243101172471238722310116230347472301286GK239909299001-010

2024

10.23919/JSEE.2024.000095

系统工程与电子技术(英文版)
中国航天科工防御技术研究院 中国宇航学会 中国系统工程学会 中国系统仿真学会

系统工程与电子技术(英文版)

CSTPCD
影响因子：0.64
ISSN：1004-4132
年,卷(期)：2024.35(5)