本文關(guān)鍵詞：因果圖的兩種不精確推理探索　出處：《重慶師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 不確定性推理 因果圖 證據(jù)理論 區(qū)間分析

【摘要】：不確定性問題作為人工智能最核心的研究任務(wù),將不確定性問題的求解方法大致分為兩類:一類是基于概率的方法,一類是基于非概率的方法。因果圖推理是一種概率的方法,因果圖以圖形的方式表達(dá)復(fù)雜系統(tǒng)的因果關(guān)系。由于因果圖推理中存在不確定性問題,為了更形象的將系統(tǒng)的不確定性進(jìn)行表達(dá),本文主要是研究因果圖的兩種不精確推理:將概率矩陣近似處理轉(zhuǎn)化為精確概率值;將求基本事件精確概率值擴(kuò)充為求區(qū)間概率。主要內(nèi)容如下:(1)把一個(gè)復(fù)雜系統(tǒng)用因果圖知識(shí)表達(dá),進(jìn)行系統(tǒng)故障診斷時(shí),用節(jié)點(diǎn)事件表示故障源,用有向邊表示因果關(guān)系。由于子變量的賦值狀態(tài)數(shù)不同,將因果圖分為單值因果圖和多值因果圖。將傳統(tǒng)因果圖推理用于多值因果圖中會(huì)出現(xiàn)概率不歸一的現(xiàn)象,因此提出一種多值因果圖的不精確推理。該推理方法是根據(jù)因果影響程度找到連接事件概率值,而該概率值是在引入了事件缺省狀態(tài),并假設(shè)事件各狀態(tài)之間互斥的情況下求得的。根據(jù)概率矩陣中事件各狀態(tài)發(fā)生的概率找到其發(fā)生的可能性大小,再進(jìn)行概率分配,使概率滿足歸一性,將多值因果圖轉(zhuǎn)化為單值因果圖。(2)因果圖作為一種基于概率的知識(shí)表達(dá)方法,是對(duì)基本事件發(fā)生概率已知時(shí)進(jìn)行推導(dǎo)計(jì)算,而實(shí)際應(yīng)用中,由于數(shù)據(jù)的誤差、缺失,專家的主觀偏見等很難獲得精確概率值,針對(duì)此情況本文提出將精確值擴(kuò)充為區(qū)間數(shù)。根據(jù)Dempster-Shafer證據(jù)理論(簡(jiǎn)稱D-S理論),將專家知識(shí)或者系統(tǒng)數(shù)據(jù)進(jìn)行融合,通過計(jì)算得到似然函數(shù)Pls(Plausibility Function)和信任函數(shù)Bel(Belief Function),將其分別作為概率區(qū)間的上下界,形象表達(dá)系統(tǒng)的模糊性和不確定性,同時(shí)還降低了獲取精確概率值的難度。
[Abstract]:As the core research task of artificial intelligence, uncertainty problem is divided into two categories: one is probabilistic method. One is based on non-probabilistic method. Causal graph reasoning is a probabilistic method. Causality diagrams express the causality of complex systems graphically. There is uncertainty in causal graph reasoning. In order to express the uncertainty of the system more vividly, this paper mainly studies two kinds of inexact reasoning of causality diagram: the approximate processing of probabilistic matrix is transformed into exact probabilistic value; The main contents are as follows: (1) A complex system is represented by causality diagram knowledge, and the node event is used to represent the fault source in system fault diagnosis. Use directed edges to represent causality. Due to the number of assigned states of subvariables. The causality diagram is divided into single value causality diagram and multivalued causality diagram. The phenomenon of probability disunity will occur when traditional causality diagram reasoning is used in multivalued causality diagram. Therefore, an inexact reasoning method of multi-valued causality graph is proposed. The method is to find the probability value of connected event according to the degree of causality influence, and the probability value is to introduce the default state of event. According to the probability of the occurrence of each state in the probability matrix, the probability of occurrence is found, and then the probability distribution is carried out to make the probability meet the normalization. As a method of knowledge representation based on probability, the multi-valued causality diagram is transformed into a single-valued causality diagram. It is a method to derive and calculate the probability of occurrence of a basic event when the probability is known, but it is applied in practice. It is difficult to obtain the accurate probability value because of the error of the data, the lack of the data, the subjective bias of the expert and so on. According to the Dempster-Shafer evidence theory (D-S theory for short), the expert knowledge or system data are fused. The likelihood function (Pls(Plausibility function) and the trust function (Bel(Belief function) are obtained. It is regarded as the upper and lower bound of the probability interval to express the fuzziness and uncertainty of the system, and at the same time, the difficulty of obtaining the exact probability value is reduced.
【學(xué)位授予單位】：重慶師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;O211

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 趙越;董春玲;張勤;;動(dòng)態(tài)不確定因果圖用于復(fù)雜系統(tǒng)故障診斷[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年05期

2 王影;李春好;;貝葉斯因果圖的構(gòu)建與應(yīng)用[J];統(tǒng)計(jì)與決策;2016年07期

3 韓德強(qiáng);楊藝;韓崇昭;;DS證據(jù)理論研究進(jìn)展及相關(guān)問題探討[J];控制與決策;2014年01期

4 祁武超;邱志平;;基于區(qū)間分析的結(jié)構(gòu)非概率可靠性優(yōu)化設(shè)計(jì)[J];中國(guó)科學(xué):物理學(xué) 力學(xué) 天文學(xué);2013年01期

5 張勤;;Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Discrete DAG Cases[J];Journal of Computer Science & Technology;2012年01期

6 熊彥銘;李世玲;楊戰(zhàn)平;;基于超橢球模型的故障樹區(qū)間分析方法[J];系統(tǒng)工程與電子技術(shù);2011年12期

7 王世鵬;;煤礦瓦斯爆炸事故魚刺圖分析[J];煤礦安全;2011年02期

8 張勤;;DUCG:一種新的動(dòng)態(tài)不確定因果知識(shí)的表達(dá)和推理方法(Ⅰ):離散、靜態(tài)、證據(jù)確定和有向無(wú)環(huán)圖情況[J];計(jì)算機(jī)學(xué)報(bào);2010年04期

9 任劍;高陽(yáng);;基于區(qū)間運(yùn)算的隨機(jī)多準(zhǔn)則決策方法[J];系統(tǒng)工程與電子技術(shù);2010年02期

10 林立廣;陳建軍;馬娟;劉國(guó)梁;段寶巖;;大型星載天線展開系統(tǒng)故障樹的區(qū)間分析方法[J];機(jī)械強(qiáng)度;2010年01期

相關(guān)碩士學(xué)位論文 前2條

1 彭霜霜;區(qū)間數(shù)因果圖的不確定性推理及算法研究[D];重慶師范大學(xué);2016年

2 楊佳婧;動(dòng)態(tài)不確定因果圖在化工過程監(jiān)控與故障診斷中的應(yīng)用[D];北京化工大學(xué);2014年

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本文編號(hào)：1391901