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451.
针对滚动轴承振动信号的非平稳特性和现实中难以获得大量典型故障样本的实际情况,提出基于集合经验模态分解(EEMD)能量熵和最小二乘支持向量机(LS-SVM)的滚动轴承故障诊断方法。首先通过EEMD分解将非平稳的原始振动信号分解成若干个平稳的固有模态函数(IMF);滚动轴承同一部位发生不同严重程度的故障时,在不同频带内的信号能量值会发生改变,因此可通过计算振动信号的EEMD能量熵判断发生故障的严重程度;从包含主要故障信息的IMF分量中提取的能量特征作为输入来建立支持向量机,判断滚动轴承的技术状态和故障严重程度,并选用不同核函数对诊断效果进行分析比较。实验结果表明,该方法能有效地应用于滚动轴承的故障诊断。 相似文献
452.
结合混沌的相空间重构理论和最小二乘支持向量机(LS-SVM)的优点,提出了一种基于混沌LS-SVM风功率预测方法,利用误差评价函数形成反馈机制,通过误差反馈建立参数合理的风功率预测模型。通过对实际数据的仿真,结果表明,该文所提出的混沌LS-SVM预测模型有较好的非线性拟合能力,有较高的预测精度。 相似文献
453.
航空铝合金三维端铣表面粗糙度的LS-SVM控制研究 总被引:1,自引:0,他引:1
为提高加工工件的表面质量,需要有效控制加工工件表面粗糙度,因此有必要建立精度高、泛化能力强的表面粗糙度预测模型。首先基于具有位错动力学物理基础的Z-A材料本构模型,建立航空铝合金7050材料的三维端面铣削有限元仿真模型,并设计正交试验验证有限元模型的可靠性;其次建立最小二乘支持向量机(LS-SVM)预测模型,以仿真所提供的样本数据为输入,拟合铣削参数与表面粗糙度的复杂非线性关系,实现了表面粗糙度的预测,结果表明LS-SVM模型预测的相对误差不超过6%;最后基于LS-SVM表面粗糙度预测模型得出各铣削参数对表面粗糙度的影响,为生产实际提供指导。 相似文献
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455.
The support vector machine (SVM) is a powerful classifier which has been used successfully in many pattern recognition problems. It has also been shown to perform well in the handwriting recognition field. The least squares SVM (LS-SVM), like the SVM, is based on the margin-maximization principle performing structural risk minimization. However, it is easier to train than the SVM, as it requires only the solution to a convex linear problem, and not a quadratic problem as in the SVM. In this paper, we propose to conduct model selection for the LS-SVM using an empirical error criterion. Experiments on handwritten character recognition show the usefulness of this classifier and demonstrate that model selection improves the generalization performance of the LS-SVM. 相似文献
456.
457.
图像插值过程实际上是利用已有的像素点来估计与这些像素点相邻的未知像素的颜色。可将一幅图像视为由像素坐标映射而成的二维颜色曲面。这样插值问题就可以视为由已知的像素构成的颜色曲面函数的回归问题。而基于统计学习理论(SLT)的支持向量机(SVM)方法为解决这种高维非线性问题提供了一种新的途经。该文讨论了最小二乘向量机实现数字图像插值的应用方法,并进行了图像放大的试验验证。试验表明:基于RBF核的最小二乘向量机回归技术对数字图像的插值具有很强的适应性,其性能与已有的双立方插值技术相当。 相似文献
458.
Traditional methods on creating diesel engine models include the analytical methods like multi-zone models and the intelligent based models like artificial neural network (ANN) based models. However, those analytical models require excessive assumptions while those ANN models have many drawbacks such as the tendency to overfitting and the difficulties to determine the optimal network structure. In this paper, several emerging advanced machine learning techniques, including least squares support vector machine (LS-SVM), relevance vector machine (RVM), basic extreme learning machine (ELM) and kernel based ELM, are newly applied to the modelling of diesel engine performance. Experiments were carried out to collect sample data for model training and verification. Limited by the experiment conditions, only 24 sample data sets were acquired, resulting in data scarcity. Six-fold cross-validation is therefore adopted to address this issue. Some of the sample data are also found to suffer from the problem of data exponentiality, where the engine performance output grows up exponentially along the engine speed and engine torque. This seriously deteriorates the prediction accuracy. Thus, logarithmic transformation of dependent variables is utilized to pre-process the data. Besides, a hybrid of leave-one-out cross-validation and Bayesian inference is, for the first time, proposed for the selection of hyperparameters of kernel based ELM. A comparison among the advanced machine learning techniques, along with two traditional types of ANN models, namely back propagation neural network (BPNN) and radial basis function neural network (RBFNN), is conducted. The model evaluation is made based on the time complexity, space complexity, and prediction accuracy. The evaluation results show that kernel based ELM with the logarithmic transformation and hybrid inference is far better than basic ELM, LS-SVM, RVM, BPNN and RBFNN, in terms of prediction accuracy and training time. 相似文献
459.
460.