Abstract
Inlet start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection controls of scramjets. In ground and flight tests, it is inevitably to introduce the sensor noises to the measurement system. How to overcome or weaken the influence of the sensor noises and the outer disturbances is an important issue to the control system of the engine. To solve this problem, the 2D inner steady flow of hypersonic inlets was numerically simulated in different freestream conditions and backpressures, and two different inlet unstart phenomena were analysed. The membership function for hypersonic inlet start/unstart can be obtained by using probabilistic output support vector machine, and the algorithm of multiple classifiers fusion is introduced. The variations of the classification accuracy with the intensity of the sensor noises and the number of the classifier were discussed respectively. In conclusion, it is useful to introduce the algorithm of support vector machine and multiple classifiers fusion to overcome or weaken the influence of the sensor noises on the classification accuracy of hypersonic inlet start/unstart. The number of the practical fusion classifiers needs a tradeoff between the fusion classification accuracy and the complexity of the classification system.
Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition
