Inlet flow pattern recognition is one of the most crucial issues and also the foundation of protection control for supersonic air-breathing propulsion systems. This article proposes a hybrid algorithm of fast K-nearest neighbors (F-KNN) and improved directed acyclic graph support vector machine (I-DAGSVM) to solve this issue based on a large amount of experimental data. The basic idea behind the proposed algorithm is combining F-KNN and I-DAGSVM together to reduce the classification error and computational cost when dealing with big data. The proposed algorithm first finds a small set of nearest samples from the training set quickly by F-KNN and then trains a local I-DAGSVM classifier based on these nearest samples. Compared with standard KNN which needs to compare each test sample with the entire training set, F-KNN uses an efficient index-based strategy to quickly find nearest samples, but there also exists misclassification when the number of nearest samples belonging to different classes is the same. To cope with this, I-DAGSVM is adopted, and its tree structure is improved by a measure of class separability to overcome the sequential randomization in classifier generation and to reduce the classification error. In addition, the proposed algorithm compensates for the expensive computational cost of I-DAGSVM because it only needs to train a local classifier based on a small number of samples found by F-KNN instead of all training samples. With all these strategies, the proposed algorithm combines the advantages of both F-KNN and I-DAGSVM and can be applied to the issue of large-scale supersonic inlet flow pattern recognition. The experimental results demonstrate the effectiveness of the proposed algorithm in terms of classification accuracy and test time.

进气流模式识别是最关键的问题之一，也是超声速空气推进系统保护控制的基础。本文提出了一种基于快速K最近邻（F-KNN）和改进的有向无环图支持向量机（I-DAGSVM）的混合算法，以基于大量实验数据来解决此问题。该算法背后的基本思想是将F-KNN和I-DAGSVM结合在一起以减少处理大数据时的分类错误和计算成本。所提出的算法首先通过F-KNN快速从训练集中找到一小组最近的样本，然后基于这些最近的样本训练局部I-DAGSVM分类器。与标准KNN相比，标准KNN需要将每个测试样本与整个训练集进行比较，F-KNN使用有效的基于索引的策略来快速找到最近的样本，但是当属于不同类别的最近样本的数量相同时，也会存在分类错误。为了解决这个问题，采用了I-DAGSVM，并通过一种类可分离性的措施来改进其树结构，以克服分类器生成中的顺序随机化并减少分类错误。此外，该算法弥补了I-DAGSVM昂贵的计算成本，因为它仅需要基于F-KNN发现的少量样本而不是所有训练样本来训练局部分类器。通过所有这些策略，所提出的算法结合了F-KNN和I-DAGSVM的优点，可应用于大规模超音速进气道流型识别问题。