本文選題：不完備 + 條件熵�。� 參考：《重慶理工大學(xué)》2017年碩士論文

【摘要】：由于人類社會的不斷發(fā)展與進(jìn)步,人們獲取數(shù)據(jù)的方式越來越多樣化,面對形式多樣的、數(shù)量巨大的、關(guān)系復(fù)雜的、要求及時處理的這些數(shù)據(jù),如何得到有用的刪除冗余的信息,如何將得到的信息提煉后得到更精確的信息,是當(dāng)前的研究熱點之一,在電子科技和網(wǎng)絡(luò)工程的很多領(lǐng)域中,得到精確和完備的信息是很困難的,采集到的數(shù)據(jù)通常包含噪聲,模糊且不完整。那么,當(dāng)采集到的信息都是模糊或不完整的情況下,如何將不完備的多個信息源進(jìn)行融合就成為了多傳感器信息融合技術(shù)的重點。隨著科學(xué)研究的不斷深入,對不同環(huán)境下各種數(shù)據(jù)的處理就顯得尤為重要,本文基于此背景,在不同環(huán)境下的序信息系統(tǒng)中,通過邏輯運(yùn)算算子構(gòu)建了幾種新的粗糙模糊集模型,并研究了多個模糊或不完整的信息源如何進(jìn)行融合的方法。主要創(chuàng)新點如下:1.在序信息系統(tǒng)以及直覺模糊序信息系統(tǒng)中,將變精度和程度粗糙集模型通過邏輯運(yùn)算算子結(jié)合起來構(gòu)建三種新的粗糙模糊集模型,并通過實際案例驗證了所構(gòu)建模型的有效性。2.研究了當(dāng)多個信源都是不完備時,也即是當(dāng)每一個信息系統(tǒng)均為不完備的信息系統(tǒng)時,如何進(jìn)行信息融合的方法,并根據(jù)提出的融合方法設(shè)計了對應(yīng)的算法。然后,基于UCI數(shù)據(jù)集設(shè)計了一系列實驗,將本文提出的融合方法和傳統(tǒng)的融合方法進(jìn)行比較,進(jìn)一步驗證了本文提出的融合方法在精度方面具有十分明顯的優(yōu)勢。3.將不完備信息系統(tǒng)的融合方法應(yīng)用到模糊信息系統(tǒng)中,通過定義一種新的相似性度量,構(gòu)造了一種新的相似二元關(guān)系,進(jìn)而在此基礎(chǔ)上建立了融合模型。
[Abstract]:As a result of the continuous development and progress of human society, the ways in which people obtain data are becoming more and more diversified. In the face of these data, which are diverse in form, large in quantity, complicated in relation, and require timely processing, How to get useful redundant information, how to extract the information and get more accurate information is one of the current research hotspots, in many fields of electronic science and technology and network engineering, It is difficult to obtain accurate and complete information. The collected data usually contain noise, blur and incomplete. Then, when the information collected is fuzzy or incomplete, how to fuse incomplete multiple information sources has become the focus of multi-sensor information fusion technology. With the development of scientific research, it is very important to deal with all kinds of data in different environments. Several new rough fuzzy set models are constructed by logical operators, and how to fuse multiple fuzzy or incomplete information sources is studied. The main innovations are as follows: 1. In order information system and intuitionistic fuzzy order information system, three new rough fuzzy set models are constructed by combining variable precision and degree rough set models through logical operators. This paper studies the method of information fusion when many information sources are incomplete, that is, every information system is incomplete, and the corresponding algorithm is designed according to the proposed fusion method. Then, a series of experiments based on UCI data set are designed, comparing the fusion method proposed in this paper with the traditional fusion method, and further validating that the fusion method proposed in this paper has a very obvious advantage in accuracy. The fusion method of incomplete information system is applied to fuzzy information system. By defining a new similarity measure, a new similarity binary relation is constructed, and then a fusion model is established.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TP202

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本文編號：1875071