Classifier utility modeling and analysis of hypersonic inlet start/unstart considering training data costs

Start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection control of scramjet. The inlet start/unstart detection can be attributed to a standard pattern classification problem, and the training sample costs have to be considered for the classifier modeling as the CFD numerical simulations and wind tunnel experiments of hypersonic inlets both cost time and money. To solve this problem, the CFD simulation of inlet is studied at first step, and the simulation results could provide the training data for pattern classification of hypersonic inlet start/unstart. Then the classifier modeling technology and maximum classifier utility theories are introduced to analyze the effect of training data cost on classifier utility. In conclusion, it is useful to introduce support vector machine algorithms to acquire the classifier model of hypersonic inlet start/unstart, and the minimum total cost of hypersonic inlet start/unstart classifier can be obtained by the maximum classifier utility theories.

► We obtain the classifier model of the hypersonic inlet start/unstart by support vector machine.
► We extend the classification criterion of hypersonic inlet start/unstart.
► We define the total cost of the classifier of hypersonic inlet start/unstart, which depends on the number of training samples and cost ratio.
► The optimal training set size, which is not sensitive to the exact value of cost ratio can be gained.
► We obtain the minimum total cost of inlet start/unstart classifier by the maximum classifier utility theories.