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Modeling Building of Miniature Unmanned Helicopter for Hovering Status Based on Local Least Square Support Vector Machine

机译:基于局部最小二乘支持向量机的悬停状态悬停状态的微型无人直升机建模构建

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Miniature unmanned helicopter (MUH) is a controlled member which is very complicated, due to their some characteristics such as highly nonlinear, close coupled, time-variation, open-loop unstable etc. The traditional method of identification is a whole model method. Although those can solve some hard problem, the time-variation is not treated well. The paper introduces a method of model building for miniature unmanned helicopter (MUH), based on local least square support vector machine. Namely the nearest samples to the predicted sample are selected online, and model building is finished by those samples with prediction. The feature of this method is that using the idea of local model building updates the model online, and the global model building brings the low ability of model generalization. In the last, compared with the traditional method of least square support vector machine in the experiment, the results show the algorithm is more effective.
机译:微型无人直升机(MUH)是一个非常复杂的受控构件,由于它们的一些特性,例如高度非线性,紧密耦合,时变,开环等等等特征。传统的识别方法是整体模型方法。虽然那些可以解决一些难题,但时间变异没有很好地处理。本文介绍了一种模型建筑,用于微型无人直升机(MUH),基于局部最小二乘支持向量机。即最近的预测样本的样本在线选择,并且模型建筑物由具有预测的样本完成。这种方法的特征是,使用本地模型构建的想法更新在线模型,全局模型建筑带来了模型泛化的低能力。在最后,与实验中最小二乘支持向量机的传统方法相比,结果表明该算法更有效。

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