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The Research Of Application Of RBF Neural Networks In Data Mining

Posted on:2010-12-03Degree:MasterType:Thesis
Country:ChinaCandidate:X W TongFull Text:PDF
GTID:2178330332481964Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
With the wide application of databases and sharp development of Internet, the capacity of utilizing information technology to manufacture and collect data has improved greatly. It is an urgent problem to mine useful information or knowledge from large databases or data warehouses. Therefore, data mining technology is developed rapidly to meet the need. But data mining (DM) often faces so much data which is noisy, disorder and nonlinear. Fortunately, artificial neural network (ANN) is suitable to solve the before-mentioned problems of DM because ANN has such merits as good robustness, adaptability, parallel-disposal, distributing-memory and high tolerating-error.This paper described a simple data mining and the basic theory of artificial neural network. In analyzing the various techniques of data mining based on neural network in data mining applications in research and analysis, and then focus on RBF neural network based on the classification of data mining methods. Genetic algorithms and RBF neural networks combination of genetic algorithm to optimize the use of center values and width of the hidden layer of RBF; At the same time, an improved genetic algorithm, experiments show that the genetic algorithm to improve the RBF neural network for data mining approach can improve network capacity and classification accuracy.The followings are the main contents.(1) RBF neural networks introduce the working principle and performance of data mining analysis.(2) Of genetic algorithm analysis and research, in view of the existence of the basic genetic algorithm and local search easier precocious ability, such as disadvantage, through the genetic algorithm in the crossover operator and mutation in part to improve the comparison of their search in the same environment differences in performance.(3) The adoption of improved genetic algorithm to optimize the RBF neural network center and the width of the experiment proved that the use of this method can be up to a certain extent improve network capacity and accuracy of approximation.
Keywords/Search Tags:Data mining, RBF Neural network, Genetic algorithm, Improved Genetic algorithm
PDF Full Text Request
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