Study Of Some Problems About Rough Sets And Neural Network Model Based On Rough Sets

Rough set theory has emerged as a major mathematical method to manage uncertainty which is from inexact, noisy and incomplete information. The thesis places emphasis on study of the basic problems about rough set theory—real-value attributes quantization and attributes reduction. The research about integrating genetic algorithm with rough set theory is deep and effective. Using the results concluded from the combined algorithms determining neural networks parameters can make up the shortages of neural networks. The constructed method of neural networks based on genetic algorithm and rough set method is proposed.The main contents are shown as follows.Firstly, the method of real-value attributes quantization is studied and another method based rough set theory and genetic algorithm is brought forward. The new method is the hybrid of these two intelligent algorithms. The experiment result has successfully shown that it is possible to integrate rough set theory with a GA-based search algorithm to perform quantization group from examples with continuous data. The quantization group of attributes produced is simple and concise, and the training time decreases greatly.Secondly, the methods of attributes reduction using rough set theory are introduced and another method based rough set theory and genetic algorithm is brought forward. Rough set makes the data into an information system, and then genetic algorithm begins to look for the reduction attributes group. Rough set theory provides a novel way of dealing with vagueness and uncertainty. When coupled with genetic algorithm, the reduction attributes group is able to be obtained from inconsistent information. The new method has these merits. The experiment results have successfully shown that it is possible to integrate rough set theory with a GA-based search algorithm to perform attributes reduction efficiently.Thirdly, the structure and the traits of BP neural network are researched. The possibility of the integration of rough set method and neural network based theshortage of BP network is discussed. The advantages of rough set theory, in dealing with the problems of uncertainties and incompleteness, are been deep into study. Another new method based on the rough set theory, genetic algorithm and neural network is worked out, and the architecture, the algorithm, and the performances of this method are researched and analyzed in detailed, a large number of simulative experiments are carried on. And as the results show, some satisfied performances are given by the method of neural network based on rough sets, such as the performance in dealing with the problem of real-time. Some key difficulties of theory and technique of the method, which mentioned in the thesis, are solved well. In fact, the hybrid of three intelligent algorithms is great significance.Finally, the summary of the whole work is given, while some of unsolved problems in the thesis and the prospect of the further study are indicated.