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An improved SVM classifier based on multi-verse optimizer for fault diagnosis of autopilot

机译:一种基于多元优化器的改进SVM分类器,用于自动驾驶仪故障诊断

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With regard to the lack of the sample of faults in the test of autopilot, a model of fault diagnosis based on support vector machine (SVM) optimized by multi-verse optimizer (MVO) is put forward. SVM does well in solving the few samples and nonlinear problem, which is suitable for the fault diagnosis of autopilot. To solve the overfitting and underfitting resulted from the improper parameters of SVM, multi-verse optimizer was applied to optimizing the parameters of SVM. By this way, a model of fault diagnosis with better performance was built. The simulation experiment results show that the accuracy of SVM based on MVO can achieve 98.3673% using 50 training samples. However, the accuracy of genetic algorithm (GA)-SVM achieves 91.0204% and the accuracy of SVM based on gravitational search algorithm (GSA) achieves 91.6327%. The simulation experiment results shows that SVM based on MVO has much better performance than others.
机译:针对自动驾驶仪测试中缺乏故障样本的问题,提出了一种基于多向量优化器(MVO)优化的基于支持向量机(SVM)的故障诊断模型。支持向量机在解决少量样本和非线性问题方面做得很好,适用于自动驾驶仪的故障诊断。为了解决由于支持向量机参数不正确而导致的过度拟合和欠拟合问题,采用多诗节优化器对支持向量机的参数进行优化。通过这种方式,建立了性能更高的故障诊断模型。仿真实验结果表明,使用50个训练样本,基于MVO的支持向量机的精度可以达到98.3673%。然而,遗传算法(SVM)的精度达到91.0204%,基于重力搜索算法(GSA)的SVM的精度达到91.6327%。仿真实验结果表明,基于MVO的SVM的性能要好于其他。

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