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MODELING OF FLEXIBLE STRUCTURES BY MEANS OF LEAST SQUARE SUPPORT VECTOR MACHINE

机译:最小二乘支持向量机在柔性结构建模中的应用。

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This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support vector machine algorithm to model the flexible cantilever is demonstrated. The acquired model is able to provide accurate prediction of the system output under different operating conditions. Experimental results demonstrate the efficiency and high precision of the proposed approach.
机译:本文利用最小二乘支持向量机(LS-SVM)算法研究柔性结构的建模。建模是为任何给定系统获取合适的基于模型的控制器的第一步。使用LS-SVM算法基于实验数据对柔性结构进行精确建模,对物理系统的了解较少。与其他软计算算法相比,最小二乘支持向量机算法可以实现全局唯一解决方案。同样,LS-SVM算法需要更少的训练时间。本文证明了成功地使用支持向量机算法对柔性悬臂进行建模。所获得的模型能够在不同的操作条件下提供对系统输出的准确预测。实验结果证明了该方法的有效性和高精度。

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