發(fā)布時間：2018-05-31 01:01

  本文選題：動態(tài)貝葉斯 + 動態(tài)增長模型�。� 參考：《計算機工程與設(shè)計》2017年06期

【摘要】：針對研制階段測試性增長實驗數(shù)據(jù)"小子樣、多階段、異總體"的特點導(dǎo)致測試性水平難以驗證與評價的問題,提出一種優(yōu)化的動態(tài)貝葉斯方法。引入新Dirichlet分布構(gòu)造一個故障檢測率的動態(tài)增長模型;引入D-S區(qū)間證據(jù)推理理論融合同一階段的多個專家信息,在此基礎(chǔ)上得到置信度更高的先驗區(qū)間,用非線性優(yōu)化理論擬合先驗信息求解模型中的超參數(shù);利用貝葉斯信息融合理論推斷故障檢測率的多元聯(lián)合后驗分布,采用Gibbs抽樣求解高維后驗積分。實例對比分析結(jié)果表明,該方法有效地融合了區(qū)間型的專家信息,提高了評價結(jié)果的置信度,為研制階段測試性驗證與評價的研究提供了一種理論依據(jù)和解決方案。
[Abstract]:In order to solve the problem that the testability level is difficult to verify and evaluate due to the characteristics of "small sample, multi-stage, different population" in the experimental data of testability growth in the development phase, an optimized dynamic Bayesian method is proposed. The new Dirichlet distribution is introduced to construct a dynamic growth model of fault detection rate, and D-S interval evidential reasoning theory is introduced to fuse multiple expert information in the same stage, and on this basis, a priori interval with higher confidence is obtained. The nonlinear optimization theory is used to fit the transcendental information and the Bayesian information fusion theory is used to infer the multivariate joint posterior distribution of the fault detection rate and the Gibbs sampling is used to solve the high dimensional posterior integrals. The results of comparison and analysis show that the method effectively integrates the interval type of expert information and improves the confidence of the evaluation results. It provides a theoretical basis and a solution for the research of testability verification and evaluation in the development phase.
【作者單位】： 鄭州大學(xué)信息工程學(xué)院;
【分類號】：O212.8
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本文編號：1957607