發(fā)布時(shí)間：2018-04-10 21:25

  本文選題：粗糙集 + 二進(jìn)制區(qū)分矩陣��； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：知識(shí)約簡(jiǎn)是粗糙集理論的核心內(nèi)容之一。通過(guò)知識(shí)約簡(jiǎn)可以在保證信息系統(tǒng)決策和分類能力不變的前提下剔除數(shù)據(jù)集中冗余信息�，F(xiàn)實(shí)生活中,數(shù)據(jù)以不可預(yù)期的速度在增加。每獲得一個(gè)新對(duì)象數(shù)據(jù),在冗余信息剔除計(jì)算中都對(duì)整個(gè)數(shù)據(jù)集重新進(jìn)行知識(shí)約簡(jiǎn)計(jì)算,必然是浪費(fèi)時(shí)間和低效的。因此,對(duì)于以原有決策表知識(shí)約簡(jiǎn)計(jì)算結(jié)果為基礎(chǔ),計(jì)算新增加部分從而獲得新決策表知識(shí)約簡(jiǎn)的增量式知識(shí)約簡(jiǎn)算法具有重要的理論和現(xiàn)實(shí)意義。本文針對(duì)傳統(tǒng)二進(jìn)制區(qū)分矩陣存儲(chǔ)空間大以及如何有效地將二進(jìn)制矩陣在完備和不完備信息系統(tǒng)中用于增量式知識(shí)約簡(jiǎn)的問(wèn)題,研究了基于二進(jìn)制區(qū)分矩陣的增量式知識(shí)約簡(jiǎn)算法,并將約簡(jiǎn)算法用于光伏發(fā)電功率預(yù)測(cè)系統(tǒng)的數(shù)據(jù)預(yù)處理,主要研究?jī)?nèi)容包括:(1)探索了在完備信息系統(tǒng)下基于二進(jìn)制區(qū)分矩陣的增量式屬性約簡(jiǎn)算法。為了解決二進(jìn)制區(qū)分矩陣存儲(chǔ)空間大的問(wèn)題,提出了一種壓縮二進(jìn)制區(qū)分矩陣的方法,將二進(jìn)制區(qū)分矩陣的存儲(chǔ)空間從|C|+1列簡(jiǎn)化成3列。當(dāng)增加單個(gè)新實(shí)例時(shí),根據(jù)建立的壓縮二進(jìn)制區(qū)分矩陣,通過(guò)動(dòng)態(tài)更新二進(jìn)制區(qū)分矩陣的方法實(shí)現(xiàn)增量式屬性求核,并以屬性核為出發(fā)點(diǎn),提出了在增加單個(gè)實(shí)例時(shí)基于二進(jìn)制區(qū)分矩陣的增量式屬性約簡(jiǎn)算法。(2)探索了在完備信息系統(tǒng)下增加成組數(shù)據(jù)時(shí)基于二進(jìn)制區(qū)分矩陣的增量式屬性約簡(jiǎn)算法。根據(jù)新增數(shù)據(jù)是單一實(shí)例還是成組實(shí)例對(duì)象,選擇不同的分支程序來(lái)更新二進(jìn)制區(qū)分矩陣。根據(jù)更新后的二進(jìn)制區(qū)分矩陣快速求核,并以屬性核為出發(fā)點(diǎn),提出了適用于成組對(duì)象增加的基于二進(jìn)制區(qū)分矩陣的增量式屬性約簡(jiǎn)算法。(3)探索了基于二進(jìn)制區(qū)分矩陣的不完備信息系統(tǒng)增量式屬性約簡(jiǎn)算法。不完備信息系統(tǒng)下的增量式屬性約簡(jiǎn)求解首先需要求解容差類。當(dāng)在已有系統(tǒng)中新增實(shí)例時(shí),為了快速求解新的容差類,首先提出了一種快速、穩(wěn)定性較好的容差類靜態(tài)求解方法,然后在此基礎(chǔ)上提出了容差類的增量式求解方法。根據(jù)增量式求得的新容差類,通過(guò)動(dòng)態(tài)更新二進(jìn)制區(qū)分矩陣,提出了不完備信息系統(tǒng)下基于二進(jìn)制區(qū)分矩陣的增量式屬性約簡(jiǎn)算法。(4)探索了增量式屬性約簡(jiǎn)算法用于光伏發(fā)電功率預(yù)測(cè)數(shù)據(jù)的特征提取。對(duì)采集的光伏數(shù)據(jù)建立光伏發(fā)電功率預(yù)測(cè)數(shù)據(jù)模型決策表,并對(duì)采集到的光伏數(shù)據(jù)進(jìn)行相應(yīng)的離散化處理。當(dāng)新增數(shù)據(jù)時(shí)采用增量式屬性約簡(jiǎn)算法進(jìn)行特征提取,并對(duì)提取特征數(shù)據(jù)采用神經(jīng)網(wǎng)絡(luò)算法進(jìn)行訓(xùn)練和預(yù)測(cè)。
[Abstract]:Knowledge reduction is one of the core contents of rough set theory.The redundant information in the data set can be eliminated by knowledge reduction on the premise that the decision and classification ability of the information system is invariable.In real life, data is increasing at an unexpected rate.It is a waste of time and inefficiency to recompute the knowledge reduction of the whole data set in the computation of redundant information elimination for each new object data.Therefore, it is of great theoretical and practical significance for the incremental knowledge reduction algorithm to obtain the new decision table knowledge reduction based on the results of the original decision table knowledge reduction.This paper aims at the problem of large storage space of traditional binary discernibility matrix and how to effectively apply binary matrix to incremental knowledge reduction in complete and incomplete information systems.The incremental knowledge reduction algorithm based on binary discernibility matrix is studied, and the reduction algorithm is applied to the data preprocessing of photovoltaic power prediction system.The main research contents include: 1) the incremental attribute reduction algorithm based on binary discernibility matrix in complete information system is explored.In order to solve the problem of large storage space of binary discernibility matrix, a method of compressing binary discernibility matrix is proposed. The storage space of binary discernibility matrix is simplified from C1 column to 3 column.When a single new instance is added, according to the compressed binary discriminant matrix, the incremental attribute kernel is realized by dynamically updating the binary discernibility matrix, and the starting point is attribute kernel.An incremental attribute reduction algorithm based on binary discernibility matrix is proposed when adding a single instance. The incremental attribute reduction algorithm based on binary discernibility matrix is explored when adding group data in a complete information system.According to whether the new data is a single instance or a group instance object, different branch programs are selected to update the binary discriminant matrix.Based on the updated binary discernibility matrix, the kernel is quickly obtained and the attribute kernel is used as the starting point.An incremental attribute reduction algorithm based on binary discernibility matrix is proposed for adding groups of objects. The incremental attribute reduction algorithm for incomplete information systems based on binary discernibility matrix is explored.In order to solve incremental attribute reduction in incomplete information systems, tolerance classes should be solved first.In order to solve the new tolerance class quickly, a fast and stable static solution method of tolerance class is proposed, and then an incremental solution method of tolerance class is proposed.According to the new tolerance class obtained by the increment formula, the binary discriminant matrix is dynamically updated.An incremental attribute reduction algorithm based on binary discernibility matrix in incomplete information systems is proposed. The incremental attribute reduction algorithm is explored for feature extraction of photovoltaic power prediction data.The model decision table of photovoltaic power prediction data is established for the collected photovoltaic data, and the corresponding discrete processing of the collected photovoltaic data is carried out.When new data is added, incremental attribute reduction algorithm is used for feature extraction, and neural network algorithm is used to train and predict feature data.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

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