The Feature Selection Algorithms For Fluoride Powder Liquefaction Modeling

Fluoride powder containing large amounts of water is inclined to be liquefied, which will lead to the shipping wreck. As more and more accidents happen, researchers begin to investigate the causes of fluoride powder liquefaction. In the case of the few studies on the fluoride powder liquefaction both in research and industry domains, this thesis takes the initiate to study this topic. Firstly, the thesis considers all possible factors that affect the liquefaction of the fluoride powder. Then the feature selection method for selecting the main factors of the fluoride powder liquefaction is proposed, and an intelligent model of fluoride powder liquefaction is established. The main contributions of this thesis are provided as follows,(1) In order to find the main factors that affect the fluoride powder liquefaction, the thesis considers all possible factors that affect the liquefaction of fluorine powder. Some experiments under the relevant experimental standard are conducted with collecting 196 samples, each of which contains 20 properties for further investigation.(2) The progressive abnormal sample deletion method based on regression forecasting error is presented. Compared to the conventional abnormal sample deletion methods, the proposed algorithm gives the samples identified as abnormality a second testing opportunity, which could avoid the incorrect deletion of some normal samples as abnormal in large part. Meanwhile, this algorithm can dramatically improve the accuracy of regression forecasting model results.(3) The feature selection method based on regression forecasting error and genetic algorithm is presented. Processes of the algorithm are described as follows: Firstly, all of the properties of the fluoride powder liquefaction are encoded as a genetic entity. Secondly, this thesis evaluates the fitness of the individual using a fitness function based on regression forecasting error and the number of the properties belonging to this individual. Finally, an optimal individual will be selected through the repeated processes of selection, crossover and mutation. The properties of this individual are the prominent properties which affect the fluoride powder liquefaction. In the genetic algorithm, the selection and mutation operators are improved by introducing the simulated annealing algorithm, which enhances the global searching capabilities of the genetic algorithm.(4) With the feature selection methods proposed in this thesis for the analysis of experimental data, the optimal attribute set containing eight properties is obtained. Furthermore, a regression forecasting model is presented for the sensitivity analysis by using aforementioned eight properties data.