Research On The Dynamic Learning Model Of Case-based Reasoning And Its Application On TE Process

Case-based reasoning(CBR), as a new reasoning technology and machine learning method, has been successfully applied on the pattern recognition, intelligent control, decision support and other fields. Especially in the pattern classification field, the performance of the CBR system, such as the classification accuracy, classification efficiency and the classification reliability are the key factors that affect the success of the application of CBR. Therefore, to improve the performance of CBR and achieve a CBR system with high accuracy, high efficiency is of great significance. However, in the process of building the CBR model, there exists some theoretical problems, such as the limitation of the CBR’s creative thinking ability caused by lazy learning mechanism and the lack of theory support on constructing the dynamic CBR learning model. These problems have been widely concentrated on and still remain unsolved, which restrict the comprehensive improvement of CBR performance. Therefore, further investigation on the CBR is of great importance. Aiming at the four steps that affect the learning ability of CBR, namely case retrieve, case reuse, case revise and case retain, an improved CBR classifier with dynamic learning ability is constructed, where water-filling principle, case evaluation link, selective case maintenance strategy are introduced. The detailed contents are listed as follows:(1) Aiming at the weak learning ability caused by the statistical weight in the retrieval process. A dynamic iterative weight distribution method is proposed, which combines the water-filling principle in the wireless communication field and the thought of fraction programming: firstly, this method regards each attribute as sub channels, utilizes the volatility parameters of each attribute to achieve the initial statistical weight; then the iterative adjustment indexes are constructed according to the information in the nearest neighbors, to get the final weight result, which lays a reliable basis for the similarity calculation and the following reasoning steps.(2) In order to solve the bad reuse performance brought by the traditional reuse mechanism of CBR system, an evaluation link is added into the 4R cycle(after case reuse), where a dynamic evaluation-revise strategy is proposed: firstly, acquire the suggested solution through case reuse, according to which divide the nearest neighbors into the two set, one is a set of which the cases have the same category with the target case, the other one is the set of which cases have different categories with the target case; secondly, trustworthiness indexes are proposed to calculate the specific trustworthiness value of the suggested solution, then a trustworthy threshold is set to evaluate the case as trustworthy or untrustworthy; at last, the cases in trustworthy set could directly reuse the suggested solution and restored in the case base, while the untrustworthy set should conduct the following case revise step. As for the case revise, a strategy based on second retrieval is proposed to obtain the final result of the untrustworthy set. The evaluation link decreases the uncertainty and potential risks that untrustworthy cases may bring, which improves the learning ability and reliability of CBR system.(3) As for the low efficiency problem caused by the incremental learning mechanism of CBR, a dynamic case maintenance strategy based on case selection is proposed: firstly, a feature reduction method based on threshold optimization is proposed, where genetic algorithm is adopted to search an optimized weight threshold, according to which could remove the attribute whose weight is lower than the threshold to accomplish the feature reduction step; then the absorption and removal criteria are introduced to direct the case maintenance: select the cases that satisfy the rules to construct the subset to reduce the case base size. The proposed case maintenance strategy could avoid the utility problem, reduce the bad influence caused by redundant cases and harmful cases, decrease the time complexity, and guarantee the reliability of CBR.(4) In order to test the effectiveness of methods proposed in the dissertation, some experiments are conducted with 10 datasets from UCI data base, where 10-fold cross validation method is deployed in the experiments to test the effectiveness of the dynamic iterative weight distribution learning method based on water-filling, the evaluation-revise strategy based on trustworthiness, and the case maintenance strategy base on selection respectively. The experimental results show that the improved CBR could achieve a better classification performance, which verify that the proposed methods could effectively improve the accuracy, efficiency and the dynamic learning ability of CBR. In the end, CBR model with dynamic learning ability is utilize to build a diagnosis system for the application on TE pro-cess, the results(diagnosis accuracy, diagnosis sensitivity, specificity, ROC) on the faults of TE process indicate that the proposed methods could provide a satisfying diagnosis performance, which proves the effectiveness and superiority of CBR on improving the learning performance and reliability of CBR system, providing the methodology foundation for similar industrial processes.