MARGInS Model-Based Analysis of Realizable Goals in Systems

He, Yuning

关键词: AEROSPACE SYSTEMS;    CHARACTERIZATION;    COMPLEX SYSTEMS;    COMPUTERIZED SIMULATION;    DESIGN ANALYSIS;    FACTOR ANALYSIS;    MACHINE LEARNING;    NEURAL NETS;    SAFETY FACTORS;    SYSTEMS ANALYSIS;    SYSTEMS ENGINEERING;    TIME SERIES ANALYSIS;   

RP-ID  :  ARC-E-DAA-TN74357

学科分类：软件

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

The high complexity of modern aircraft and spacecraft requires elaborate Verification and Validation (V&V) approaches to make sure that such complex systems work properly and reliably. MARGInS is a framework for the analysis, understanding, and prediction of the behavior of a complex, hybrid system. MARGInS contains a set of machine learning and statistical algorithms for multivariate clustering, treatment learning, critical factor determination, time-series analysis, event prediction, and safety-boundary detection and characterization. The framework supports system testing and can be configured to find novel features in test suites, determine classes of behavior, propose new experiments that can efficiently explore and characterize the boundaries between classes of system behavior, and to create visualizations and reports.