Study On Aerocraft Assessment System Based On Expert System

The current Expert System is difficult of obtaining knowledge automatically, lackof learning mechanism, and inefficity to reason. As for these problem, this paperin-depth studied on related technology and algorithm, which included knowledgeacquisition based on MapReduce, Cased-Based Reasoning, Cases Similarity algorithm,Case-Reduction algorithm, the rule representation and fact representation based onRule-Based Reasoning, reasoning mechanism, and RETE Pattern Matching algorithm.In addition, this paper proposed some new model and improved algorithm, in order tosolve the problem in aerocraft assessment field, that included difficulty for processingfuzzy knowledge and poorness of timeliness.The number of aerocraft parameters(attributes) was huge, but few parameters wereimportant in the pattern classification and assessment, therefor it was meaningful tostudy how to extract key parameters from historical data, so as to analysis andsummarize assessment rules. The knowledge extraction techniques based on Rough Sethad ability to acquire key parameters from numerous parameters, and the classicalalgorithm can only load small data set into memory to process at one time, however,which can not process huge massive data set likc aerocraft historical data. This paperargued that the knowledge extraction techniques based on Rough Set can be parallelcomputing, and this paper builded a knowledge extraction model based on MapReducein order to calculate the importance measure of parameters. Finally, this paper held arelated experment on the Hadoop, that showed this technique can efficiently processmassive data like aerocraft historical data.The aerocraft rules were immature, the relationship between parameters werecomplexity and ambiguous, as a result, the reasoning mechanism can not useNon-White or Black logic and Two valued logic from Remote Data to top facts. In orderto calculate the status and confidence of top facts, this paper proposed a method basedon Case-Based Reasoning, namely to assess the current status of aircraft according tohistorical data. Firstly, this paper introduced the current commonly used casessimilarity algorithms, such as enumeration type distance, Euclidean distance,ontology-based semantic similarity, the curves similarity. For the lackness of classicalKNN algorithm, this paper proposed improved KNN algorithm based on probability distribution and weights. The utility problem will occurs after the Case-BasedReasoning system runs many times, and this problem results in a decrease performance,such as a large storage space, a low retrieval rapid. To solve this proble, this paperproposed Memory Algorithm of Case Base Based on Utility Value and Cases ReductionAlgorithm Based on Support Vector.With the study on the aerocraft assessment, the number of rules expertssummarized become more and more, as a result, the knowledge base is inconsitent andthe reasoning is inefficient. Therefore, this paper studied the stucture of uncertaintyRule-Based expert system, and introduced some related key points, which included theknowledge representation, the source of uncertainty in the aerocraft assessment, thespreading of uncertainty, forward reasoning, the consistency maintenance of knowledgebase, explaination mechanism and so on. After analysising the classical RETE algorithmand commomly used improvement stragegies, this paper proposed improved RETEalgorithm based on Cost Model, which can automatically find the optimal RETEtopology, and reduces intermediate nodes, and greatly reduced RETE algorithm’s timecomplexity and space complexity.To solve the problem in the aerocraft assessment task, which includes the amountof data is huge, the assessment process is complex, and the conclusion of the assessmentis inaccurate, an aircraft assessment system based on hybrid reasoning model isproposed. The system comprises a plurality of independent module on function, and itcomplete reasoning and searching results from the original data of aerocraft to finalassessment conclusion. This paper presents assessment tree and trigger pointmechanism, in order to meet real-time requirements. According to the characteristics ofthe aircraft assessment, the top-facts are get using case-based reasoning to fully utilizeits non-precise reasoning and self-learning advantages; reasoning in the assessmentstage is use of accurate and efficient rule-based inference mechanism. Practical exampleusing historical data show that, the proposed assessment system performs very well, andcan real-time complete aerocraft assement, and the whole process is automatic.