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Research On Data Mining Technology In Process Control And Probe Of Intelligent Control Strategy

Posted on:2006-12-01Degree:DoctorType:Dissertation
Country:ChinaCandidate:C ZhaoFull Text:PDF
GTID:1118360152490838Subject:Control theory and control engineering
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Data mining technology is the combination of artificial intelligence, database and statistical theory which has the comparatively extensive application prospects. It is predicted by experts that there will be some revolutionary progress respecting data mining in the future decade, and the data mining is to be the key technology for commerce analysis, rule discovery, discerning and analyzing users' information in real time.The purpose of data mining is to find out the meaningful mode from the data. The mode can be a set of rules, clusters, decision trees, and the knowledge relying on the network or any other ways.Rough Sets Theory, put forward by Professor Pawlak at the beginning of the eighties of the 20th century, is a Mathematics tool used to cope with uncertainty and fuzzy knowledge. Its basic thought is to lead out the categorized rule of the concept through knowledge reduction on the premise of keeping categorized ability unchanging. It does not need to offer any prior information outside the set of relevant data, suitable to finding the implicit, potential and useful rule in the data, and finding out the relevant relation and characteristic of its inside data. In recent years, great achievement has been made in Rough Sets Theory and its application. The Theory has already become the important branch of the soft computing technology, with its field covering pattern-recognition, machine study, decision analysis and support, knowledge acquisition and knowledge discovering.When Rough Sets Theory is applied into data mining technology, various kinds of rules excavated through the employment of knowledge reduction of Rough Sets and the simplification of data are of great significance to the research of intelligence control strategy of the complicated system.This paper, on the basis of the probe of Rough Sets Theory and data mining technology, studies on several data mining methodologies used in the industrial process control, with its main innovative outcome including:Proposal of the enlightening reduction algorithm and approximate reduction algorithm based on Rough Sets Theory, got rid of by attribute reduction and redundant rule; in the meanwhile, aimed for the industrial process control, an approximate reduction algorithm on the basis of sampling is expounded, from which an approximate reduction of lower error rate can be quickly found out, having a realistic meaning to the real-time control.Proposal of the data mining algorithm controlling the variables coupling degree and linguistic association rules in the industrial process by utilization of the data mining technology. The linguistic association rules are in line with human beings' thinking mode, favourable to the set-up of different intelligent control model;Set-up of data mining controlling model provides the application of data mining technology with a systematic technological methodology. A kind of practical data mining model is set up for the industrial process control; at the same time, with the employment of data mining application terrace, the research focus respecting data mining is transferred from finding out the methodology to the systematic application;Soft computing technology, including data mining, fuzzy logic, neural network, rough sets theory and etc. , is one of the most important approaches in solution to the complicated system modeling and controlling. Through the set-up of the rouhg fuzzy model, rogh neural network model, the intelligent control strategy has been initially studied.
Keywords/Search Tags:Data Mining, Rough Sets, Attribute Reduction, Coupling Relations, Association Rules, Intelligent Control
PDF Full Text Request
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