KEYWORDS: Data modeling, Modeling, Error analysis, Design and modelling, Mathematical modeling, Education and training, Statistical methods, Reflection, Probability theory, Matrices
This paper predicts the success probability of successful or unsuccessful multivariate tests, uses Lasso regression method to fit the input data, mining information from different dimensions of input data to analyze the success probability, and makes experiments to compare and verify the effectiveness of the methods.
KEYWORDS: Reliability, Data fusion, Computer simulations, Systems modeling, Probability theory, Failure analysis, Statistical analysis, Information fusion, Analytical research, Data processing
In this paper, we use Uncertainty Quantification method to analyze the data from three types of success-failure type test sources: physical test, semi-physical test and computer simulation test. Bayes method is used to evaluate the data fusion of multiple source test, and the validity and applicability of the method are verified by experiments.
KEYWORDS: Principal component analysis, Machine learning, Weapons, Telecommunications, Warfare, Process modeling, Instrument modeling, Dimension reduction, Decision support systems, Systems modeling
This paper mainly provides a method to evaluate the contribution rate of equipment system based on machine learning. Firstly, the index system of contribution rate evaluation of equipment system is established, the requirements of evaluation are clarified, and the normalized data is used as input. The evaluation model based on Principal Component Analysis (PCA) is built, and the contribution rate of the system is sorted by weight calculation. This paper uses experiments to verify the validity of our method.
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