本文選題：限制容差關(guān)系 + 程度多粒度粗糙集　； 參考：《安徽工業(yè)大學(xué)》2017年碩士論文

【摘要】：由于網(wǎng)絡(luò)技術(shù)和通信技術(shù)的飛速發(fā)展,涌現(xiàn)出類型多樣的海量數(shù)據(jù),各個(gè)領(lǐng)域都期待從海量的、雜亂無(wú)章的、噪聲數(shù)據(jù)中獲取有用的知識(shí)。粗糙集(rough set,RS)在獲取模糊性、不確定的知識(shí)方面展現(xiàn)出巨大的優(yōu)勢(shì)。它不需要任何其它先驗(yàn)知識(shí)和附加信息,依靠數(shù)據(jù)集合本身的屬性,便可以挖掘出數(shù)據(jù)中隱含的有價(jià)值的信息。多粒度粗糙集(Multi-Granulation Rough Set,MGRS)是一種新的粗糙集擴(kuò)展模型,它從多個(gè)粒度空間對(duì)目標(biāo)概念進(jìn)行近似逼近,在邊界區(qū)域的范圍縮小,目標(biāo)概念的表示精度提高方面,具有明顯的優(yōu)勢(shì)。實(shí)際生活中,由于測(cè)量偏差等因素,常常存在一些不完備的,但隱藏著豐富知識(shí)的數(shù)據(jù)。為了從不完備信息系統(tǒng)中獲得更加準(zhǔn)確的知識(shí),本文結(jié)合程度粗糙集,研究不完備MGRS模型和粒度約簡(jiǎn)方法。本文主要工作如下:(1)介紹經(jīng)典粗糙集的基礎(chǔ)知識(shí),給出一些實(shí)例形象地解釋粗糙集的基本概念。針對(duì)完備系統(tǒng)和不完備系統(tǒng),介紹目前MGRS的發(fā)展與研究現(xiàn)狀。(2)分別介紹了基于容差關(guān)系、相似關(guān)系、限制容差關(guān)系的單粒度粗糙集拓展模型和MGRS拓展模型,分析不同關(guān)系下各個(gè)粗糙集模型的優(yōu)缺點(diǎn)。(3)針對(duì)不完備信息系統(tǒng),提出基于限制容差關(guān)系的程度MGRS,包括程度樂(lè)觀MGRS和程度悲觀MGRS。分析程度樂(lè)觀MGRS和程度悲觀MGRS的不足之處,提出一種基于限制容差關(guān)系的可變程度MGRS模型。研究這三種模型的相關(guān)性質(zhì)與聯(lián)系。通過(guò)實(shí)例和實(shí)驗(yàn)分析可變程度MGRS的優(yōu)越性。(4)考慮粒度的權(quán)重,基于限制容差關(guān)系,提出不完備加權(quán)程度MGRS,并討論其性質(zhì)。定義不完備加權(quán)程度MGRS的粒度矩陣、核粒度和粒度重要性公式。提出一種粒度約簡(jiǎn)方法,在獲取核粒度的基礎(chǔ)上,以粒度重要性作為啟發(fā)式信息選擇粒度,獲得最終的粒度約簡(jiǎn)集。
[Abstract]:Due to the rapid development of network technology and communication technology, massive data of various types have emerged. All fields expect to obtain useful knowledge from mass, disorderly and noisy data. Rough set sets (RS) show great advantages in acquiring fuzzy and uncertain knowledge. It does not need any other prior knowledge and additional information. It can mine the valuable information hidden in the data by relying on the attributes of the data set itself. Multi-granulation Rough set (MGRS) is a new rough set extension model, which approximates the concept of target from multiple granularity spaces, and has obvious advantages in reducing the range of boundary area and improving the precision of representation of target concept. In real life, because of the measurement deviation and other factors, there are often some incomplete, but hidden knowledge of the data. In order to obtain more accurate knowledge from incomplete information system, this paper studies incomplete MGRS model and granularity reduction method combining degree rough set. The main work of this paper is as follows: (1) introduce the basic knowledge of classical rough sets and give some examples to explain the basic concepts of rough sets graphically. For complete systems and incomplete systems, the development and research status of MGRS are introduced. (2) the single granularity rough set extension model and MGRS extension model based on tolerance relation, similarity relation and limiting tolerance relation are introduced respectively. This paper analyzes the advantages and disadvantages of each rough set model under different relationships. (3) aiming at incomplete information systems, the degree MGRS based on restricted tolerance relationship is proposed, including degree optimistic MGRS and degree pessimistic MGRs. By analyzing the disadvantages of degree optimistic MGRS and degree pessimistic MGRS, a variable degree MGRS model based on restricted tolerance relationship is proposed. The related properties and relationships of these three models are studied. The advantage of variable degree MGRS is analyzed by examples and experiments. Considering the weight of granularity, based on the limited tolerance relation, the incomplete weighted degree MGRS is proposed and its properties are discussed. The granularity matrix, kernel granularity and granularity importance formula of incomplete weighted degree MGRS are defined. A granularity reduction method is proposed. Based on the kernel granularity, granularity importance is used as heuristic information to select granularity, and the final granularity reduction set is obtained.
【學(xué)位授予單位】：安徽工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李聰;;多粒度模糊粗糙集研究[J];數(shù)學(xué)雜志;2016年01期

2 冀素琴;石洪波;呂亞麗;;基于粒計(jì)算與區(qū)分能力的屬性約簡(jiǎn)算法[J];模式識(shí)別與人工智能;2015年04期

3 孟慧麗;馬媛媛;徐久成;;基于信息量的悲觀多粒度粗糙集粒度約簡(jiǎn)[J];南京大學(xué)學(xué)報(bào)(自然科學(xué));2015年02期

4 郭郁婷;;基于限制容差關(guān)系變精度的β多粒度粗糙集[J];閩南師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年01期

5 孫文鑫;劉玉鋒;;一般多粒度模糊粗糙集模型[J];重慶師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年04期

6 張明;程科;楊習(xí)貝;唐振民;;基于加權(quán)粒度的多粒度粗糙集[J];控制與決策;2015年02期

7 吳志遠(yuǎn);鐘培華;胡建根;;程度多粒度粗糙集[J];模糊系統(tǒng)與數(shù)學(xué);2014年03期

8 景運(yùn)革;李天瑞;;一種基于關(guān)系矩陣的決策表正域約簡(jiǎn)算法[J];計(jì)算機(jī)科學(xué);2013年11期

9 陳媛;楊棟;;基于信息熵的屬性約簡(jiǎn)算法及應(yīng)用[J];重慶理工大學(xué)學(xué)報(bào)(自然科學(xué));2013年01期

10 張明;唐振民;徐維艷;楊習(xí)貝;;可變多粒度粗糙集模型[J];模式識(shí)別與人工智能;2012年04期

相關(guān)碩士學(xué)位論文 前1條

1 陳青梅;多粒度概率粗糙集若干問(wèn)題研究[D];廣西大學(xué);2014年

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