Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel i...Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel in SVM is drawn in a more natural way by using the fuzzy logic method proposed in this paper. This method provides easy hardware implementation and straightforward interpretability. Experiments on two typical chaotic time series predictions have been carried out and the obtained results show that the average CPU time can be reduced significantly at the cost of a small decrease in prediction accuracy, which is favourable for the hardware implementation for chaotic time series prediction.展开更多
支持向量机中对核函数的要求为对称的半正定矩阵.来自于神经网络的sigm o id核函数在其参数满足一定条件时才成为半正定矩阵,但是这种核函数在SVM中却有很多成功的应用.本文将sigm o id核函数与模糊逻辑相结合并使其模糊化,从而简化了SV...支持向量机中对核函数的要求为对称的半正定矩阵.来自于神经网络的sigm o id核函数在其参数满足一定条件时才成为半正定矩阵,但是这种核函数在SVM中却有很多成功的应用.本文将sigm o id核函数与模糊逻辑相结合并使其模糊化,从而简化了SVM的计算并便于用硬件实现.通过对混沌时间序列预测以及图像去噪滤波器两个实例的实验研究发现,使用模糊sigm o id核函数可以使SVM回归建模在损失较小精度的代价下,较大地降低平均CPU执行时间。展开更多
支持向量机因其相比于传统算法具有良好的分类性能,而广泛地应用于故障诊断研究中。但标准SVM存在训练时间长,占用内存大的不足。近似支持向量机(Proximal Support Vec-tor Machines,PSVM)算法具有训练速度快占用内存少的特点,特别适用...支持向量机因其相比于传统算法具有良好的分类性能,而广泛地应用于故障诊断研究中。但标准SVM存在训练时间长,占用内存大的不足。近似支持向量机(Proximal Support Vec-tor Machines,PSVM)算法具有训练速度快占用内存少的特点,特别适用于大量数据的故障诊断。但其对于分类超平面附近点的诊断精度略显不足。针对此类问题文中将耗时较少的Vague-Sigmoid核函数应用于PSVM,用以提高其对于在分类面附近样本的分类精度,仿真证明获得了较好的效果。展开更多
文摘Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel in SVM is drawn in a more natural way by using the fuzzy logic method proposed in this paper. This method provides easy hardware implementation and straightforward interpretability. Experiments on two typical chaotic time series predictions have been carried out and the obtained results show that the average CPU time can be reduced significantly at the cost of a small decrease in prediction accuracy, which is favourable for the hardware implementation for chaotic time series prediction.
文摘支持向量机中对核函数的要求为对称的半正定矩阵.来自于神经网络的sigm o id核函数在其参数满足一定条件时才成为半正定矩阵,但是这种核函数在SVM中却有很多成功的应用.本文将sigm o id核函数与模糊逻辑相结合并使其模糊化,从而简化了SVM的计算并便于用硬件实现.通过对混沌时间序列预测以及图像去噪滤波器两个实例的实验研究发现,使用模糊sigm o id核函数可以使SVM回归建模在损失较小精度的代价下,较大地降低平均CPU执行时间。
文摘支持向量机因其相比于传统算法具有良好的分类性能,而广泛地应用于故障诊断研究中。但标准SVM存在训练时间长,占用内存大的不足。近似支持向量机(Proximal Support Vec-tor Machines,PSVM)算法具有训练速度快占用内存少的特点,特别适用于大量数据的故障诊断。但其对于分类超平面附近点的诊断精度略显不足。针对此类问题文中将耗时较少的Vague-Sigmoid核函数应用于PSVM,用以提高其对于在分类面附近样本的分类精度,仿真证明获得了较好的效果。
