Research And Application Of Knowledge Acquirement Methods For Intelligent Diagnosis Expert System

Knowledge acquirement is the bottleneck of expert system. Machine learning is the most current knowledge acquirement methods .Machine learning methods based on the Neural Network (NN) and Rough Sets (RS) theory are applied to acquire knowledge for intelligent expert system and detailed study about that has been done in this thesis.Firstly, influence factors of generalization of neural network are presented in this thesis, in order to improve neural network's generalization ability and dynamic knowledge acquirement adaptive ability, a structure auto-adaptive Neural Network new model based on genetic algorithm is proposed to optimize structure parameter of NN including hidden layer nodes, training epochs, initial weights, and so on;Secondly,through establishing integrating Neural Network and introducing data fusion technique,the integrality and precision of acquired knowledge is greatly improved.then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement, knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system, by completing missing value data and eliminating unnecessary attributes, discretization of continuous attribute, reducing redundancy , extracting rules in this thesis. Finally, Rough sets theory and Neural Network are combined to form RNN (Rough Neural Network) model for acquiring knowledge, in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement, and RNN can improve the speed and quality of knowledge acquirement greatly.In the thesis, international standard data and simulation data are applied to test and verify the knowledge acquirement model, and the algorithms of knowledge acquirement are applied for aero-engine intelligent diagnosis expert system. Examples, and experiment show the validity and accuracy of the model proposed in this thesis.The knowledge acquirement model proposed in this paper effectively breakthrough the bottleneck of the intelligent diagnosis expert system and provide a solid foundation for applicability intelligent expert diagnosis system.