The Crossing Research On Rough Set And Its Application In Traditional Chinese Medicine Diagnosis

Rough Set works as an effective mathematic tool for dealing with uncertain and unholonomic data. Its theory and application get quick development in past ten years. The crossing researches on reduction and rule extraction are the main points of this dissertation, which are funded by State Administration of Traditional Chinese Medicine (A97105).Firstly, a new attributes reduction strategy (ARS) is discussed. This method combines the number of attributes values into attributes reduction, which reduces the time complexity and the space complexity. It's advantage can be showed more clearly when dealing with a great number of data.Secondly, ARS is used to reduct the neurons in input layer and hidden layer for optimizing BP Neural Network structure. During reduction process, attributes reduction strategy based on weight (ARS-W), when the significance of every attribute itself is different, is used in reducting the neurons in hidden layer. This method takes effect; Fuzzy Multi-Factorial Evaluation is used in BP Network reduction structure evaluation, which offers a criterion for BP Network structure.Thirdly, ARS is used to reduct the structure of input layer , third layer and fourth layer to optimize Fuzzy Neural Network. This method offers a good solution to dimension disaster in input space and original rules exponential growth that may occur in traditional Fuzzy Neural Network original structure.Fourthly, an exploration of Rough Set in decentralized control is discussed. This method makes full use of sample data, extract the control rules of the decentralized controller. This method doesn't need fuzzification and defuzzification. So the control is simple and convenient.