本文選題：多源信息融合 + 貝葉斯網(wǎng)絡(luò)�。� 參考：《山東大學(xué)》2017年碩士論文

【摘要】：隨著多傳感器感知技術(shù)、智能計(jì)算技術(shù)以及無(wú)線通信技術(shù)的飛速發(fā)展,軍事領(lǐng)域率先提出了"數(shù)據(jù)融合"這一概念,也就是將各式各樣的傳感器所采集到的信息加以"融合處理",進(jìn)而能夠獲得比單個(gè)或單一種類的傳感器更加行之有效的信息,因此對(duì)于多源信息融合技術(shù)的研究得到了社會(huì)的廣泛關(guān)注。本論文主要進(jìn)行了以下三個(gè)方面的研究:1.多源信息融合中連續(xù)變量離散化算法的研究本論文以基于概率論的貝葉斯網(wǎng)絡(luò)數(shù)據(jù)融合算法為核心,針對(duì)現(xiàn)有算法對(duì)連續(xù)變量的離散化處理不夠精確的問(wèn)題,提出基于等期望值標(biāo)準(zhǔn)狀態(tài)概率分配的連續(xù)變量離散化算法,更加科學(xué)合理地實(shí)現(xiàn)了對(duì)連續(xù)變量的無(wú)損處理,該算法解決了現(xiàn)有離散化方法的離散效果不夠精確的問(wèn)題。2.多源信息融合中多屬性權(quán)重分配算法的研究多屬性信息融合是多源信息融合的主要內(nèi)容�，F(xiàn)有的融合算法不可避免地遇到了各屬性對(duì)于決策影響力即權(quán)重不同的問(wèn)題,目前大多數(shù)權(quán)重分配算法或太過(guò)依賴專家知識(shí)或太過(guò)注重變量的統(tǒng)計(jì)特性,缺乏理論依據(jù),只是定性地分析各連續(xù)屬性的相對(duì)影響力的大小已不能滿足現(xiàn)代數(shù)據(jù)融合定量分析的要求。本論文提出了一種基于信息增益的多屬性權(quán)重分配算法,主要依據(jù)信息論,利用信息增益定量地計(jì)算權(quán)重,解決了現(xiàn)有算法定性分析所導(dǎo)致的權(quán)重計(jì)算不夠精確的問(wèn)題。3.加入權(quán)重的貝葉斯網(wǎng)絡(luò)算法研究貝葉斯網(wǎng)絡(luò)基于概率論,其參數(shù)學(xué)習(xí)方式是可監(jiān)督式的。為了得到更加準(zhǔn)確的推理結(jié)果,本論文采用了兩種加入權(quán)重的方式:一是特征級(jí)的權(quán)重加入,即將權(quán)重加入到證據(jù)信息當(dāng)中,進(jìn)而得到權(quán)重化的證據(jù)信息后再進(jìn)行網(wǎng)絡(luò)推理;二是決策級(jí)的權(quán)重加入,由于貝葉斯網(wǎng)絡(luò)支持不完備證據(jù)的特性,所以我們可以將各參數(shù)變量單獨(dú)加入貝葉斯網(wǎng)絡(luò)進(jìn)行推理,最后將權(quán)重加入到它們的推理結(jié)果之中。仿真分析證明,加入權(quán)重能夠有效地提升貝葉斯網(wǎng)絡(luò)的推理準(zhǔn)確率。通過(guò)對(duì)基于等期望值標(biāo)準(zhǔn)狀態(tài)概率分配的連續(xù)變量離散化算法的研究,本論文實(shí)現(xiàn)了連續(xù)變量的無(wú)損處理,同時(shí)也將貝葉斯網(wǎng)絡(luò)的應(yīng)用環(huán)境拓展到了連續(xù)變量的推理融合之中。通過(guò)對(duì)基于信息增益的多屬性權(quán)重分配算法的研究,本論文為權(quán)重分配帶來(lái)了理論依據(jù),能夠?qū)崿F(xiàn)定量地計(jì)算多連續(xù)屬性的權(quán)重。通過(guò)對(duì)加入權(quán)重的貝葉斯網(wǎng)絡(luò)算法的研究,本論文能夠有效地提高貝葉斯網(wǎng)絡(luò)推理的準(zhǔn)確率,為貝葉斯網(wǎng)絡(luò)的應(yīng)用推廣提供了堅(jiān)實(shí)的基礎(chǔ)。
[Abstract]:With the rapid development of multi-sensor sensing technology, intelligent computing technology and wireless communication technology, the concept of "data fusion" has been first put forward in the military field. That is, to "fuse" the information collected by a variety of sensors, and thus to obtain more effective information than a single or a single type of sensor. Therefore, the research of multi-source information fusion technology has been widely concerned by the society. This paper mainly carries on the following three aspects of research: 1. Research on discretization algorithm of continuous variables in Multi-source Information Fusion; this paper focuses on Bayesian network data fusion algorithm based on probability theory, aiming at the problem that the existing algorithms are not accurate in the discretization of continuous variables. A discretization algorithm for continuous variables based on standard state probability assignment of equal expectation value is proposed, which realizes the lossless treatment of continuous variables more scientifically and reasonably. The algorithm solves the problem that the discretization effect of existing discretization methods is not accurate enough. Research on Multi-attribute weight allocation algorithm in Multi-source Information Fusion; Multi-attribute Information Fusion is the main content of Multi-source Information Fusion. The existing fusion algorithms inevitably encounter the problem that the attributes have different influence on decision-making, that is, the weight is different. At present, most of the weight allocation algorithms either rely too much on the expert knowledge or pay too much attention to the statistical characteristics of variables, so they lack the theoretical basis. But qualitative analysis of the relative influence of each continuous attribute can not meet the requirements of modern data fusion and quantitative analysis. In this paper, a multi-attribute weight allocation algorithm based on information gain is proposed. Based on information theory, the weight is calculated quantitatively by using information gain, which solves the problem of inaccurate weight calculation caused by qualitative analysis of existing algorithms. Bayesian Network algorithm with weight; Bayesian Network is based on probability theory and its parameter learning method is supervised. In order to obtain more accurate reasoning results, this paper adopts two ways to add weight: first, the weight of feature level is added to the information of evidence, then the weight is added to the information of evidence, and then the weighted information of evidence is obtained and then the network reasoning is carried out. Secondly, the weight of decision level is added. Because Bayesian network supports the characteristic of incomplete evidence, we can add each parameter variable to Bayesian network separately and add weight to their reasoning result. Simulation results show that adding weights can effectively improve the reasoning accuracy of Bayesian networks. By studying the discretization algorithm of continuous variables based on the standard state probability assignment of equal expectation value, this paper realizes the lossless processing of continuous variables, and extends the application environment of Bayesian network to the inference fusion of continuous variables. Through the research of multi-attribute weight allocation algorithm based on information gain, this paper provides a theoretical basis for weight allocation, and can quantitatively calculate the weight of multiple continuous attributes. Through the research of Bayesian network algorithm with weight, this paper can effectively improve the accuracy of Bayesian network reasoning, and provide a solid foundation for the application and promotion of Bayesian network.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP202

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本文編號(hào)：1960935