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

【摘要】：知識約簡是粗糙集理論的核心內(nèi)容之一。通過知識約簡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)實生活中,數(shù)據(jù)以不可預期的速度在增加。每獲得一個新對象數(shù)據(jù),在冗余信息剔除計算中都對整個數(shù)據(jù)集重新進行知識約簡計算,必然是浪費時間和低效的。因此,對于以原有決策表知識約簡計算結(jié)果為基礎(chǔ),計算新增加部分從而獲得新決策表知識約簡的增量式知識約簡算法具有重要的理論和現(xiàn)實意義。本文針對傳統(tǒng)二進制區(qū)分矩陣存儲空間大以及如何有效地將二進制矩陣在完備和不完備信息系統(tǒng)中用于增量式知識約簡的問題,研究了基于二進制區(qū)分矩陣的增量式知識約簡算法,并將約簡算法用于光伏發(fā)電功率預測系統(tǒng)的數(shù)據(jù)預處理,主要研究內(nèi)容包括:(1)探索了在完備信息系統(tǒng)下基于二進制區(qū)分矩陣的增量式屬性約簡算法。為了解決二進制區(qū)分矩陣存儲空間大的問題,提出了一種壓縮二進制區(qū)分矩陣的方法,將二進制區(qū)分矩陣的存儲空間從|C|+1列簡化成3列。當增加單個新實例時,根據(jù)建立的壓縮二進制區(qū)分矩陣,通過動態(tài)更新二進制區(qū)分矩陣的方法實現(xiàn)增量式屬性求核,并以屬性核為出發(fā)點,提出了在增加單個實例時基于二進制區(qū)分矩陣的增量式屬性約簡算法。(2)探索了在完備信息系統(tǒng)下增加成組數(shù)據(jù)時基于二進制區(qū)分矩陣的增量式屬性約簡算法。根據(jù)新增數(shù)據(jù)是單一實例還是成組實例對象,選擇不同的分支程序來更新二進制區(qū)分矩陣。根據(jù)更新后的二進制區(qū)分矩陣快速求核,并以屬性核為出發(fā)點,提出了適用于成組對象增加的基于二進制區(qū)分矩陣的增量式屬性約簡算法。(3)探索了基于二進制區(qū)分矩陣的不完備信息系統(tǒng)增量式屬性約簡算法。不完備信息系統(tǒng)下的增量式屬性約簡求解首先需要求解容差類。當在已有系統(tǒng)中新增實例時,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,通過動態(tài)更新二進制區(qū)分矩陣,提出了不完備信息系統(tǒng)下基于二進制區(qū)分矩陣的增量式屬性約簡算法。(4)探索了增量式屬性約簡算法用于光伏發(fā)電功率預測數(shù)據(jù)的特征提取。對采集的光伏數(shù)據(jù)建立光伏發(fā)電功率預測數(shù)據(jù)模型決策表,并對采集到的光伏數(shù)據(jù)進行相應(yīng)的離散化處理。當新增數(shù)據(jù)時采用增量式屬性約簡算法進行特征提取,并對提取特征數(shù)據(jù)采用神經(jīng)網(wǎng)絡(luò)算法進行訓練和預測。
[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.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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