将最小二乘支持向量机(least square support vector machine,LS-SVM)应用于航空发动机气路故障诊断。首先,分析了用于气路故障诊断的巡航偏差数 .
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将最小二乘支持向量机引入到小字符集压印字符识别中。
This paper presents an application of least squares support vector machines in small-set pressed protuberant character recognition.
将最小二乘支持向量机(LS - SVM)应用于飞机襟翼状态趋势研究。
Least square support vector machine (LS-SVM) is used to predict the trend of the aircraft flap system.
首次尝试将最小二乘支持向量机技术用于土壤侵蚀预测，并与BP神经网络的方法进行了对比，取得了较好的预测精度。
This paper try to predict soil erosion with the Least Square support vector machine technology and the better predict precision compared to the BP artificial neural network has been gotten.
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